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Atlas Vector Search

Atlas Vector Search クイック スタート

項目一覧

  • 目的
  • ベクトル検索インデックスの作成
  • ベクトル検索クエリの実行
  • 学習の概要
  • 次のステップ

このクイック スタートでは、ベクトル埋め込みを含むサンプルドキュメントを Atlas クラスターまたはローカル Atlas 配置にロードし、それらの埋め込みに Atlas Vector Search インデックスを作成し、セマンティック検索を実行してクエリに似たドキュメントを返す方法について説明します。

所要時間: 15 分

目的

このクイック スタートでは、次の手順を実行します。

  1. sample_mflix.embedded_moviesコレクションのインデックス定義を作成し、 plot_embeddingフィールドをvector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を使用して類似性を測定します。

  2. サンプルsample_mflix.embedded_moviesコレクションを検索する Atlas ベクトル検索クエリを実行します。クエリは、 $vectorSearchステージを使用してplot_embeddingフィールドを検索します。このフィールドには 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。クエリはベクトル埋め込みを使用して string時間移動を検索しplot_embedding 。最大150の最近傍を考慮し、結果として10ドキュメントを返します。

詳しくは、 ラーニングの概要 を参照してください。

ベクトル検索インデックスの作成

➤ このページの例を実行するために使用するクライアントを設定するには、右側のナビゲーション ペインにある[言語を選択] ドロップダウン メニューを使用します。

このセクションでは、Atlas クラスターまたはローカル コンピューターでホストされている配置にロードするサンプル データに Atlas Vector Search インデックスを作成します。

1

Atlas クラスターを設定します。

  1. 無料の Atlas アカウントを作成するか、既存のアカウントにサインインします。

  2. Atlas クラスターがまだない場合は、 無料の M 0クラスターを作成します。 Atlas クラスターの作成の詳細については、「 クラスターの作成 」を参照してください。

    注意

    既存のクラスターを使用している場合は、Atlas プロジェクトへのProject Data Access Admin以上のアクセス権が必要です。

    新しいクラスターを作成する場合は、必要な権限がデフォルトで付与されます。

    プロジェクト ごとに作成できるM0無料クラスターは 1 つだけプロジェクト。

  3. 左側のサイドバーで、[ Atlas Search ] をクリックします。 Select data sourceメニューからクラスターを選択し、 Go to Atlas Searchをクリックします。

  4. サンプルデータセットをまだクラスターにロードしていない場合は、[Load a Sample Dataset] をクリックします。[Load Sample Dataset] ダイアログボックスで、[Load Sample Dataset] をクリックして確定します。

    サンプルデータセットをすでに読み込んでいる場合は、sample_mflix データベースに embedded_movies コレクションが含まれていることを確認します。含まれていない場合は、サンプルデータベースを削除し、サンプルデータセットを再読み込みします。

    サンプル データセットのロードが完了するまでに数分かかる場合があります。

2

ベクトル検索 インデックスを作成します。

注意

mongoshコマンドまたはドライバーヘルパーメソッドを使用して、すべての Atlas クラスター階層に Atlas Search インデックスを作成できます。サポートされているドライバー バージョンのリストについては、 「サポートされているクライアント」 を参照してください。

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

  1. mongosh を使用して Atlas 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、Atlas 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。

  2. インデックスを作成するコレクションを含むデータベースに切り替えます。

    例

    use sample_mflix
    switched to db sample_mflix

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

    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": "euclidean"
    11      }
    12    ]
    13  }
    14);

    このインデックス定義:

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

  1. Go モジュールを初期化します。

    mkdir go-vector-quickstart && cd go-vector-quickstart
    go mod init go-vector-quickstart

  2. Go Driver 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

    インストール手順の詳細については、 MongoDB Go ドライバーのドキュメント を参照してください。

  3. インデックスを定義します。

    vector-index.goという名前のファイルを作成します。以下のコードをコピーして、ファイルに貼り付けます。

    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
    31	// Define the index details
    32	type vectorDefinitionField struct {
    33		Type          string `bson:"type"`
    34		Path          string `bson:"path"`
    35		NumDimensions int    `bson:"numDimensions"`
    36		Similarity    string `bson:"similarity"`
    37	}
    38
    39	type vectorDefinition struct {
    40		Fields []vectorDefinitionField `bson:"fields"`
    41	}
    42
    43	indexName := "vector_index"
    44	opts := options.SearchIndexes().SetName(indexName).SetType("vectorSearch")
    45
    46	indexModel := mongo.SearchIndexModel{
    47		Definition: vectorDefinition{
    48			Fields: []vectorDefinitionField{{
    49				Type:          "vector",
    50				Path:          "plot_embedding",
    51				NumDimensions: 1536,
    52				Similarity:    "euclidean"}},
    53		},
    54		Options: opts,
    55	}
    56
    57	// Create the index
    58	searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel)
    59	if err != nil {
    60		log.Fatalf("failed to create the search index: %v", err)
    61	}
    62	log.Println("New search index named " + searchIndexName + " is building.")
    63
    64	// Await the creation of the index.
    65	log.Println("Polling to check if the index is ready. This may take up to a minute.")
    66	searchIndexes := coll.SearchIndexes()
    67	var doc bson.Raw
    68	for doc == nil {
    69		cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName))
    70		if err != nil {
    71			fmt.Errorf("failed to list search indexes: %w", err)
    72		}
    73
    74		if !cursor.Next(ctx) {
    75			break
    76		}
    77
    78		name := cursor.Current.Lookup("name").StringValue()
    79		queryable := cursor.Current.Lookup("queryable").Boolean()
    80		if name == searchIndexName && queryable {
    81			doc = cursor.Current
    82		} else {
    83			time.Sleep(5 * time.Second)
    84		}
    85	}
    86
    87	log.Println(searchIndexName + " is ready for querying.")
    88}

    このインデックス定義:

    このコードには、インデックスが使用可能かどうかを確認するためのポーリング メカニズムも含まれています。

  4. <connection-string>を指定します。

    <connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

    接続stringには、次の形式を使用する必要があります。

    mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

    注意

    接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

    接続stringには、次の形式を使用する必要があります。

    mongodb://localhost:<port-number>/?directConnection=true

  5. 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 New search index named vector_index is building.
    2024/10/17 09:38:22 Polling to check if the index is ready. This may take up to a minute.
    2024/10/17 09:38:48 vector_index is ready for querying.

  1. Java ドライバーバージョン 5.2 以降を依存関係としてプロジェクトに追加します。

    • Maven を使用している場合は、pom.xml 依存関係リストに次の依存関係を追加してください。

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

    • Gradle を使用している場合は、次の依存関係を build.gradle 依存関係リストに追加します。

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

  2. Java ドライバー JAR ファイルを CLASSPATH に追加します。

    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。

  3. VectorIndex.javaという名前のファイルを作成します。以下のコードをコピーして、ファイルに貼り付けます。

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

    このインデックス定義:

    このコードには、インデックスが使用可能かどうかを確認するためのポーリング メカニズムも含まれています。

  4. <connection-string>を指定します。

    <connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

    接続stringには、次の形式を使用する必要があります。

    mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

    注意

    接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

    接続stringには、次の形式を使用する必要があります。

    mongodb://localhost:<port-number>/?directConnection=true

  5. 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
    New search index named vector_index is building.
    Polling to check if the index is ready. This may take up to a minute.
    vector_index is ready for querying.

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

  1. 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

    Tip

    このページの例では、プロジェクトが CommonJS モジュールとしてモジュールを管理していることを前提としています。 ES モジュールを使用している場合は、代わりに インポート構文を変更 する必要があります。

  2. インデックスを定義します。

    vector-index.jsという名前のファイルを作成します。以下のコードをコピーして、ファイルに貼り付けます。

    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": "euclidean"
    24             }
    25           ]
    26         }
    27     }
    28
    29     // run the helper method
    30     const result = await collection.createSearchIndex(index);
    31     console.log(`New search index named ${result} is building.`);
    32
    33     // wait for the index to be ready to query
    34     console.log("Polling to check if the index is ready. This may take up to a minute.")
    35     let isQueryable = false;
    36     while (!isQueryable) {
    37       const cursor = collection.listSearchIndexes();
    38       for await (const index of cursor) {
    39         if (index.name === result) {
    40           if (index.queryable) {
    41             console.log(`${result} is ready for querying.`);
    42             isQueryable = true;
    43           } else {
    44             await new Promise(resolve => setTimeout(resolve, 5000));
    45           }
    46         }
    47       }
    48     }
    49   } finally {
    50     await client.close();
    51   }
    52}
    53run().catch(console.dir);

    このインデックス定義:

    このコードには、インデックスが使用可能かどうかを確認するためのポーリング メカニズムも含まれています。

  3. <connection-string>を指定します。

    <connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

    接続stringには、次の形式を使用する必要があります。

    mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

    注意

    接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

    接続stringには、次の形式を使用する必要があります。

    mongodb://localhost:<port-number>/?directConnection=true

  4. 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
    New search index named vector_index is building.
    Polling to check if the index is ready. This may take up to a minute.
    vector_index is ready for querying.

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

  1. 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

    インストール手順の詳細については、 MongoDB Python ドライバーのドキュメント を参照してください。

  2. インデックスを定義します。

    vector-index.pyという名前のファイルを作成します。以下のコードをコピーして、ファイルに貼り付けます。

    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": "euclidean"
    22      }
    23    ]
    24  },
    25  name="vector_index",
    26  type="vectorSearch",
    27)
    28
    29result = collection.create_search_index(model=search_index_model)
    30print("New search index named " + result + " is building.")
    31# Wait for initial sync to complete
    32print("Polling to check if the index is ready. This may take up to a minute.")
    33predicate=None
    34if predicate is None:
    35  predicate = lambda index: index.get("queryable") is True
    36
    37while True:
    38  indices = list(collection.list_search_indexes(name))
    39  if len(indices) and predicate(indices[0]):
    40    break
    41  time.sleep(5)
    42print(result + " is ready for querying.")
    43
    44client.close()

    このインデックス定義:

    このコードには、インデックスが使用可能かどうかを確認するためのポーリング メカニズムも含まれています。

  3. <connection-string>を指定します。

    <connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

    接続stringには、次の形式を使用する必要があります。

    mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

    注意

    接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

    接続stringには、次の形式を使用する必要があります。

    mongodb://localhost:<port-number>/?directConnection=true

  4. 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
    New search index named vector_index is building.
    Polling to check if the index is ready. This may take up to a minute.
    vector_index is ready for querying.

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

  1. サンプル データの読み込みが完了したら、 Create Search Indexをクリックします。

  2. セクションで、 Atlas Vector Searchを選択し、JSON Editor をクリックします。Next

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

    この コレクション の各 ドキュメント には、stringplot_embedding としての映画のプロットの概要を含む、映画に関する情報が含まれています。これはまた、ドキュメントの フィールドに変換され、ベクトル埋め込みとして保存されています。

  4. Index Nameフィールドにvector_indexを指定します。

  5. 次のベクトル検索インデックスの定義をコピーして JSON エディターに貼り付けます。

    1{
    2    "fields": [{
    3    "type": "vector",
    4    "path": "plot_embedding",
    5    "numDimensions": 1536,
    6    "similarity": "dotProduct"
    7    }]
    8}

    このインデックス定義:

  6. [Next] をクリックします。

  7. [Create Search Index] をクリックします。

    インデックスの構築には約 1 分かかります。 ベクトル インデックスの作成が完了すると、 Status列にはActiveが読み取られます。

1

依存関係をインストールします。

詳細な手順については、「前提条件 」を参照してください。

  1. Atlas CLI をインストールします。

    Homebrew を使用する場合 、ターミナルで次のコマンドを実行できます。

    brew install mongodb-atlas-cli

    その他のオペレーティング システムへのインストール手順については、「 Atlas CLI のインストール」を参照してください。

  2. Docker をインストールします。

    Docker では、MongoDB イメージをプルしてキャッシュするためにネットワーク接続が必要です。

2

Atlas のローカル配置を設定します。

  1. Atlas アカウントをお持ちでない場合は、ターミナルでatlas setupを実行するか、新しいアカウントを作成してください。

  2. atlas deployments setupを実行し、プロンプトに従ってローカル配置を作成します。 配置への接続を求められたら、 skipを選択します。

    詳細な手順については、「ローカル Atlas 配置の作成 」を参照してください。

3

サンプル データをロードします。

  1. サンプル データをダウンロードするには、ターミナルで次のコマンドを実行します。

    curl  https://atlas-education.s3.amazonaws.com/sampledata.archive -o sampledata.archive

  2. 次のコマンドを実行してデータを配置に読み込み、 <port-number>を配置をホストしているポートに置き換えます。

    mongorestore --archive=sampledata.archive --port=<port-number>
4

ベクトル検索 インデックスを作成します。

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

  1. mongosh を使用して Atlas クラスターに接続します。

    ターミナル ウィンドウでatlas deployments connectを実行し、プロンプトに従って、 mongosh経由で Atlas のローカル配置に接続します。 接続の詳細な手順については、「 Atlas 配置のローカル配置の管理 」を参照してください。

  2. インデックスを作成するコレクションを含むデータベースに切り替えます。

    例

    use sample_mflix
    switched to db sample_mflix

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

    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": "euclidean"
    11      }
    12    ]
    13  }
    14);

    このインデックス定義:

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

  1. Go モジュールを初期化します。

    mkdir go-vector-quickstart && cd go-vector-quickstart
    go mod init go-vector-quickstart

  2. Go Driver 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

    インストール手順の詳細については、 MongoDB Go ドライバーのドキュメント を参照してください。

  3. インデックスを定義します。

    vector-index.goという名前のファイルを作成します。以下のコードをコピーして、ファイルに貼り付けます。

    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
    31	// Define the index details
    32	type vectorDefinitionField struct {
    33		Type          string `bson:"type"`
    34		Path          string `bson:"path"`
    35		NumDimensions int    `bson:"numDimensions"`
    36		Similarity    string `bson:"similarity"`
    37	}
    38
    39	type vectorDefinition struct {
    40		Fields []vectorDefinitionField `bson:"fields"`
    41	}
    42
    43	indexName := "vector_index"
    44	opts := options.SearchIndexes().SetName(indexName).SetType("vectorSearch")
    45
    46	indexModel := mongo.SearchIndexModel{
    47		Definition: vectorDefinition{
    48			Fields: []vectorDefinitionField{{
    49				Type:          "vector",
    50				Path:          "plot_embedding",
    51				NumDimensions: 1536,
    52				Similarity:    "euclidean"}},
    53		},
    54		Options: opts,
    55	}
    56
    57	// Create the index
    58	searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel)
    59	if err != nil {
    60		log.Fatalf("failed to create the search index: %v", err)
    61	}
    62	log.Println("New search index named " + searchIndexName + " is building.")
    63
    64	// Await the creation of the index.
    65	log.Println("Polling to check if the index is ready. This may take up to a minute.")
    66	searchIndexes := coll.SearchIndexes()
    67	var doc bson.Raw
    68	for doc == nil {
    69		cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName))
    70		if err != nil {
    71			fmt.Errorf("failed to list search indexes: %w", err)
    72		}
    73
    74		if !cursor.Next(ctx) {
    75			break
    76		}
    77
    78		name := cursor.Current.Lookup("name").StringValue()
    79		queryable := cursor.Current.Lookup("queryable").Boolean()
    80		if name == searchIndexName && queryable {
    81			doc = cursor.Current
    82		} else {
    83			time.Sleep(5 * time.Second)
    84		}
    85	}
    86
    87	log.Println(searchIndexName + " is ready for querying.")
    88}

    このインデックス定義:

    このコードには、インデックスが使用可能かどうかを確認するためのポーリング メカニズムも含まれています。

  4. <connection-string>を指定します。

    <connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

    接続stringには、次の形式を使用する必要があります。

    mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

    注意

    接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

    接続stringには、次の形式を使用する必要があります。

    mongodb://localhost:<port-number>/?directConnection=true

  5. 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 New search index named vector_index is building.
    2024/10/17 09:38:22 Polling to check if the index is ready. This may take up to a minute.
    2024/10/17 09:38:48 vector_index is ready for querying.

  1. Java ドライバーバージョン 5.2 以降を依存関係としてプロジェクトに追加します。

    • Maven を使用している場合は、pom.xml 依存関係リストに次の依存関係を追加してください。

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

    • Gradle を使用している場合は、次の依存関係を build.gradle 依存関係リストに追加します。

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

  2. Java ドライバー JAR ファイルを CLASSPATH に追加します。

    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。

  3. VectorIndex.javaという名前のファイルを作成します。以下のコードをコピーして、ファイルに貼り付けます。

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

    このインデックス定義:

    このコードには、インデックスが使用可能かどうかを確認するためのポーリング メカニズムも含まれています。

  4. <connection-string>を指定します。

    <connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

    接続stringには、次の形式を使用する必要があります。

    mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

    注意

    接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

    接続stringには、次の形式を使用する必要があります。

    mongodb://localhost:<port-number>/?directConnection=true

  5. 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
    New search index named vector_index is building.
    Polling to check if the index is ready. This may take up to a minute.
    vector_index is ready for querying.

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

  1. 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

    Tip

    このページの例では、プロジェクトが CommonJS モジュールとしてモジュールを管理していることを前提としています。 ES モジュールを使用している場合は、代わりに インポート構文を変更 する必要があります。

  2. インデックスを定義します。

    vector-index.jsという名前のファイルを作成します。以下のコードをコピーして、ファイルに貼り付けます。

    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": "euclidean"
    24             }
    25           ]
    26         }
    27     }
    28
    29     // run the helper method
    30     const result = await collection.createSearchIndex(index);
    31     console.log(`New search index named ${result} is building.`);
    32
    33     // wait for the index to be ready to query
    34     console.log("Polling to check if the index is ready. This may take up to a minute.")
    35     let isQueryable = false;
    36     while (!isQueryable) {
    37       const cursor = collection.listSearchIndexes();
    38       for await (const index of cursor) {
    39         if (index.name === result) {
    40           if (index.queryable) {
    41             console.log(`${result} is ready for querying.`);
    42             isQueryable = true;
    43           } else {
    44             await new Promise(resolve => setTimeout(resolve, 5000));
    45           }
    46         }
    47       }
    48     }
    49   } finally {
    50     await client.close();
    51   }
    52}
    53run().catch(console.dir);

    このインデックス定義:

    このコードには、インデックスが使用可能かどうかを確認するためのポーリング メカニズムも含まれています。

  3. <connection-string>を指定します。

    <connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

    接続stringには、次の形式を使用する必要があります。

    mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

    注意

    接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

    接続stringには、次の形式を使用する必要があります。

    mongodb://localhost:<port-number>/?directConnection=true

  4. 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
    New search index named vector_index is building.
    Polling to check if the index is ready. This may take up to a minute.
    vector_index is ready for querying.

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

  1. 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

    インストール手順の詳細については、 MongoDB Python ドライバーのドキュメント を参照してください。

  2. インデックスを定義します。

    vector-index.pyという名前のファイルを作成します。以下のコードをコピーして、ファイルに貼り付けます。

    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": "euclidean"
    22      }
    23    ]
    24  },
    25  name="vector_index",
    26  type="vectorSearch",
    27)
    28
    29result = collection.create_search_index(model=search_index_model)
    30print("New search index named " + result + " is building.")
    31# Wait for initial sync to complete
    32print("Polling to check if the index is ready. This may take up to a minute.")
    33predicate=None
    34if predicate is None:
    35  predicate = lambda index: index.get("queryable") is True
    36
    37while True:
    38  indices = list(collection.list_search_indexes(name))
    39  if len(indices) and predicate(indices[0]):
    40    break
    41  time.sleep(5)
    42print(result + " is ready for querying.")
    43
    44client.close()

    このインデックス定義:

    このコードには、インデックスが使用可能かどうかを確認するためのポーリング メカニズムも含まれています。

  3. <connection-string>を指定します。

    <connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

    接続stringには、次の形式を使用する必要があります。

    mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

    注意

    接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

    接続stringには、次の形式を使用する必要があります。

    mongodb://localhost:<port-number>/?directConnection=true

  4. 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
    New search index named vector_index is building.
    Polling to check if the index is ready. This may take up to a minute.
    vector_index is ready for querying.

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

  1. 次のファイルを作成します: vector-index.json

  2. 次のインデックス定義をコピーして、 JSONファイルに貼り付けます。

    {
        "database": "sample_mflix",
        "collectionName": "embedded_movies",
        "type": "vectorSearch",
        "name": "vector_index",
        "fields": [
            {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
            }
        ]
    }

    このインデックス定義:

  3. ファイルを保存し、ターミナルで次のコマンドを実行して、 <path-to-file>を作成したvector-index.jsonファイルへのパスに置き換えます。

    atlas deployments search indexes create --file <path-to-file>

ベクトル検索クエリの実行

このセクションでは、インデックス付き埋め込みに対してサンプルのベクトル検索クエリを実行します。

1

Data Explorerを表示するには、[ コレクション] タブをクリックします。

2

Aggregationコレクションの タブにGo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。

3

ベクトル検索クエリを作成して実行します。

  1. 集計パイプライン ペインで</> Textトグルをクリックして、パイプライン編集のテキストモードを有効にします。

  2. 次のサンプル クエリをコピーして、テキストエディタに貼り付けます。

    このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

1[
2  {
3    "$vectorSearch": {
4      "index": "vector_index",
5      "path": "plot_embedding",
6      "queryVector": 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1{
2    "score": 0.9332506656646729,
3    "plot": "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.",
4    "title": "Thrill Seekers"
5}
6{
7    "plot": "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.",
8    "title": "About Time",
9    "score": 0.9312690496444702
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11{
12    "plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.",
13    "title": "The Time Machine",
14    "score": 0.929530143737793
15}
16{
17    "plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
18    "title": "Crusade in Jeans",
19    "score": 0.9290417432785034
20}
21{
22    "title": "Timecop",
23    "score": 0.9283161759376526,
24    "plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past."
25}
26{
27    "plot": "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...",
28    "title": "A.P.E.X.",
29    "score": 0.9266218543052673
30}
31{
32    "plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
33    "title": "Men in Black 3",
34    "score": 0.9258455038070679
35}
36{
37    "score": 0.9240515828132629,
38    "plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.",
39    "title": "Tomorrowland"
40}
41{
42    "plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.",
43    "title": "Love Story 2050",
44    "score": 0.923175573348999
45}
46{
47    "plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...",
48    "title": "The Portal",
49    "score": 0.9228089451789856
50}

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

Pipeline Outputペインにはクエリの結果が表示されます。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

1

ベクトル検索クエリを作成して実行します。

  1. 次のサンプルクエリをコピーしてターミナルに貼り付け、 mongoshを使用して実行します。 ベクトル埋め込みの文字数により、クエリに貼り付けると、 mongoshは若干遅延する場合があります。

    このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

1db.embedded_movies.aggregate([
2  {
3    "$vectorSearch": {
4      "index": "vector_index",
5      "path": "plot_embedding",
6      "queryVector": 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2  {
3    plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
4    title: 'Thrill Seekers',
5    score: 0.7892671227455139
6  },
7  {
8    plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
9    title: 'About Time',
10    score: 0.7843604683876038
11  },
12  {
13    plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
14    title: 'The Time Machine',
15    score: 0.7801066637039185
16  },
17  {
18    plot: "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
19    title: 'Crusade in Jeans',
20    score: 0.7789170742034912
21  },
22  {
23    plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
24    title: 'Timecop',
25    score: 0.7771612405776978
26  },
27  {
28    plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
29    title: 'A.P.E.X.',
30    score: 0.7730885744094849
31  },
32  {
33    plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
34    title: 'Men in Black 3',
35    score: 0.7712380886077881
36  },
37  {
38    plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.',
39    title: 'Tomorrowland',
40    score: 0.7669923901557922
41  },
42  {
43    plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
44    title: 'Love Story 2050',
45    score: 0.7649372816085815
46  },
47  {
48    plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...',
49    title: 'The Portal',
50    score: 0.7640786170959473
51  }
52]

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

1

MongoDB C ドライバーをインストールします。

インストール手順の詳細については、 MongoDB C ドライバー のドキュメントを参照してください。

2

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. atlas-vector-search-quick-start.cという名前のファイルを作成し、次のサンプル クエリをコピーしてファイルに貼り付けます。

    1#include <bson/bson.h>
    2#include <mongoc/mongoc.h>
    3#include <stdio.h>
    4
    5int main(void) {
    6  
    7    mongoc_client_t *client;
    8    mongoc_collection_t *collection;
    9    mongoc_cursor_t *cursor;
    10    bson_array_builder_t *builder;
    11    bson_t *pipeline;
    12    bson_t query_vector;
    13    bson_error_t error;
    14    const bson_t *doc;
    15    char *str;
    16    
    17    int arrSize = 1536; // This collection has a vector array of 1536 dimensions; depending on your vector embedding model, this mary vary
    18    double embeddings[] = 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    19
    20    // Initialize the MongoDB C Driver
    21    mongoc_init();
    22
    23    // Connect to your Atlas collection
    24    client =  mongoc_client_new("<connection-string>");
    25    collection = mongoc_client_get_collection (client, "sample_mflix", "embedded_movies");
    26
    27    // Build query vector array
    28    builder = bson_array_builder_new();
    29    
    30    for(int i = 0; i < arrSize; i++){
    31        bson_array_builder_append_double(builder, embeddings[i]);
    32    }
    33
    34    bson_array_builder_build(builder, &query_vector);
    35    bson_array_builder_destroy(builder);
    36
    37    // Create aggregation pipeline for Vector Search
    38    pipeline = BCON_NEW(
    39        "pipeline",
    40        "[",
    41            "{",
    42                "$vectorSearch", "{", 
    43                    "index", "vector_index", 
    44                    "path", "plot_embedding", 
    45                    "queryVector", BCON_ARRAY(&query_vector),
    46                    "numCandidates", BCON_INT32(150), 
    47                    "limit", BCON_INT32(10), 
    48                "}", 
    49            "}",
    50            "{",
    51                "$project", "{", 
    52                    "title", BCON_BOOL(true),
    53                    "plot", BCON_BOOL(true),
    54                    "_id", BCON_BOOL(false),
    55                    "score", "{",
    56                        "$meta", "vectorSearchScore",
    57                    "}",
    58                "}",
    59            "}",
    60        "]"
    61    );
    62
    63    // Run query
    64    cursor = mongoc_collection_aggregate(collection, MONGOC_QUERY_NONE, pipeline, NULL, NULL );
    65  
    66    // Print results
    67    while (mongoc_cursor_next (cursor, &doc)) {
    68        str = bson_as_canonical_extended_json (doc, NULL);
    69        printf ("%s\n", str);
    70        bson_free (str);
    71    }
    72
    73    // Cleanup
    74    bson_destroy(pipeline);
    75    mongoc_cursor_destroy(cursor);
    76    mongoc_collection_destroy(collection);
    77    mongoc_client_destroy(client);
    78    mongoc_cleanup();
    79
    80    return 0;
    81}

    このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

    • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

    • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

    $projectステージは次の目的で使用されます。

    • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

    • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

3

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
4

プロジェクトに CMax を構成します。

  1. プロジェクト ディレクトリに、 CMakeLists.txtという名前のファイルを作成し、次のコードをファイルにコピーします。

    cmake_minimum_required(VERSION 3.30)
    project(atlas-vector-search-quick-start)
    # Specify the minimum version you require.
    find_package (mongoc-1.0 1.27.6 REQUIRED)
    add_executable(atlas-vector-search-quick-start
      atlas-vector-search-quick-start.c
    )
    # The "atlas-vector-search-quick-start.c" sample program is shared among four tests.
    target_link_libraries (atlas-vector-search-quick-start PRIVATE mongo::mongoc_shared)

  2. プロジェクト フォルダーにbuildというディレクトリを作成します。

    mkdir build

  3. buildディレクトリを入力し、 cmakeコマンドを実行してifyfile を作成します。

    cd build
    cmake ../
5

クエリを実行します。

buildディレクトリから、 atlas-vector-search-quick-start.cファイルをコンパイルして実行します。

cmake --build .
./atlas-vector-search-quick-start
1{ "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : { "$numberDouble" : "0.78926712274551391602" } }
2{ "plot" : "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title" : "About Time", "score" : { "$numberDouble" : "0.78436046838760375977" } }
3{ "plot" : "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title" : "The Time Machine", "score" : { "$numberDouble" : "0.78010666370391845703" } }
4{ "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : { "$numberDouble" : "0.77891707420349121094" } }
5{ "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : { "$numberDouble" : "0.77716124057769775391" } }
6{ "plot" : "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title" : "A.P.E.X.", "score" : { "$numberDouble" : "0.77308857440948486328" } }
7{ "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : { "$numberDouble" : "0.77123808860778808594" } }
8{ "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland", "score" : { "$numberDouble" : "0.76699239015579223633" } }
9{ "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : { "$numberDouble" : "0.76493728160858154297" } }
10{ "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal", "score" : { "$numberDouble" : "0.76407861709594726562" } }
1

MongoDB C++ ドライバーをプロジェクトに追加します。

  1. C++ ドライバーをインストールします。 インストール手順の詳細については、 MongoDB C++ ドライバーのドキュメント を参照してください。

  2. query-quick-startという新しいディレクトリを作成します。

    mkdir query-quick-start

    ディレクトリを入力し、 CMakeLists.txtファイルを作成します。

    cd query-quick-start
    touch CMakeLists.txt

    以下の行をコピーして、 CMakeLists.txtファイルに貼り付けます。

    cmake_minimum_required(VERSION 3.30)
    project(query_quick_start)
    set(CMAKE_CXX_STANDARD 17)
    find_package(mongocxx REQUIRED)
    find_package(bsoncxx REQUIRED)
    add_executable(query_quick_start
      atlas_vector_search_quick_start.cpp
    )
    target_link_libraries(query_quick_start PRIVATE mongo::mongocxx_shared)
    target_link_libraries(query_quick_start PRIVATE mongo::bsoncxx_shared)
2

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. atlas_vector_search_quick_start.cppという名前のファイルを作成します。

  2. 次のサンプル クエリをコピーしてatlas_vector_search_quick_start.cppファイルに貼り付けます。

    1#include <bsoncxx/builder/basic/document.hpp>
    2#include <bsoncxx/builder/list.hpp>
    3#include <bsoncxx/json.hpp>
    4#include <mongocxx/client.hpp>
    5#include <mongocxx/instance.hpp>
    6#include <mongocxx/uri.hpp>
    7
    8using bsoncxx::builder::basic::kvp;
    9using bsoncxx::builder::basic::make_document;
    10
    11int main() {
    12  try {
    13    mongocxx::instance inst{};
    14
    15    // Replace the placeholder with your Atlas connection string
    16    const auto uri = mongocxx::uri{"<connection-string>"};
    17
    18    // Connect to your Atlas cluster
    19    auto client = mongocxx::client{uri};
    20    auto db = client["sample_mflix"];
    21    auto collection = db["embedded_movies"];
    22
    23    // Define vector embeddings to search
    24    auto vectors = bsoncxx::builder::list{
    25      -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, -0.020473259, 0.004671795, -0.012555866, 0.0209531, 0.01982014, 0.024485271, 0.0105431955, -0.005178295, 0.033162415, -0.013795458, 0.007150979, 0.010243294, 0.005644808, 0.017260984, -0.0045618312, 0.0024725192, 0.004305249, -0.008197301, 0.0014203656, 0.0018460588, 0.005015015, -0.011142998, 0.01439526, 0.022965772, 0.02552493, 0.007757446, -0.0019726837, 0.009503538, -0.032042783, 0.008403899, -0.04609149, 0.013808787, 0.011749465, 0.036388017, 0.016314628, 0.021939443, -0.0250051, -0.017354285, -0.012962398, 0.00006107364, 0.019113706, 0.03081652, -0.018114036, -0.0084572155, 0.009643491, -0.0034721901, 0.0072642746, -0.0090636825, 0.01642126, 0.013428912, 0.027724205, 0.0071243206, -0.6858542, -0.031029783, -0.014595194, -0.011449563, 0.017514233, 0.01743426, 0.009950057, 0.0029706885, -0.015714826, -0.001806072, 0.011856096, 0.026444625, -0.0010663156, -0.006474535, 0.0016161345, -0.020313311, 0.0148351155, -0.0018393943, 0.0057347785, 0.018300641, -0.018647194, 0.03345565, -0.008070676, 0.0071443142, 0.014301958, 0.0044818576, 0.003838736, -0.007350913, -0.024525259, -0.001142124, -0.018620536, 0.017247654, 0.007037683, 0.010236629, 0.06046009, 0.0138887605, -0.012122675, 0.037694257, 0.0055081863, 0.042492677, 0.00021784494, -0.011656162, 0.010276617, 0.022325981, 0.005984696, -0.009496873, 0.013382261, -0.0010563189, 0.0026507939, -0.041639622, 0.008637156, 0.026471283, -0.008403899, 0.024858482, -0.00066686375, -0.0016252982, 0.027590916, 0.0051449724, 0.0058647357, -0.008743787, -0.014968405, 0.027724205, -0.011596181, 0.0047650975, -0.015381602, 0.0043718936, 0.002159289, 0.035908177, -0.008243952, -0.030443309, 0.027564257, 0.042625964, -0.0033688906, 0.01843393, 0.019087048, 0.024578573, 0.03268257, -0.015608194, -0.014128681, -0.0033538956, -0.0028757197, -0.004121976, -0.032389335, 0.0034322033, 0.058807302, 0.010943064, -0.030523283, 0.008903735, 0.017500903, 0.00871713, -0.0029406983, 0.013995391, -0.03132302, -0.019660193, -0.00770413, -0.0038853872, 0.0015894766, -0.0015294964, -0.006251275, -0.021099718, -0.010256623, -0.008863748, 0.028550599, 0.02020668, -0.0012962399, -0.003415542, -0.0022509254, 0.0119360695, 0.027590916, -0.046971202, -0.0015194997, -0.022405956, 0.0016677842, -0.00018535563, -0.015421589, -0.031802863, 0.03814744, 0.0065411795, 0.016567878, -0.015621523, 0.022899127, -0.011076353, 0.02841731, -0.002679118, -0.002342562, 0.015341615, 0.01804739, -0.020566562, -0.012989056, -0.002990682, 0.01643459, 0.00042527664, 0.008243952, -0.013715484, -0.004835075, -0.009803439, 0.03129636, -0.021432944, 0.0012087687, -0.015741484, -0.0052016205, 0.00080890034, -0.01755422, 0.004811749, -0.017967418, -0.026684547, -0.014128681, 0.0041386373, -0.013742141, -0.010056688, -0.013268964, -0.0110630235, -0.028337335, 0.015981404, -0.00997005, -0.02424535, -0.013968734, -0.028310679, -0.027750863, -0.020699851, 0.02235264, 0.001057985, 0.00081639783, -0.0099367285, 0.013522214, -0.012016043, -0.00086471526, 0.013568865, 0.0019376953, -0.019020405, 0.017460918, -0.023045745, 0.008503866, 0.0064678704, -0.011509543, 0.018727167, -0.003372223, -0.0028690554, -0.0027024434, -0.011902748, -0.012182655, -0.015714826, -0.0098634185, 0.00593138, 0.018753825, 0.0010146659, 0.013029044, 0.0003521757, -0.017620865, 0.04102649, 0.00552818, 0.024485271, -0.009630162, -0.015608194, 0.0006718621, -0.0008418062, 0.012395918, 0.0057980907, 0.016221326, 0.010616505, 0.004838407, -0.012402583, 0.019900113, -0.0034521967, 0.000247002, -0.03153628, 0.0011038032, -0.020819811, 0.016234655, -0.00330058, -0.0032289368, 0.00078973995, -0.021952773, -0.022459272, 0.03118973, 0.03673457, -0.021472929, 0.0072109587, -0.015075036, 0.004855068, -0.0008151483, 0.0069643734, 0.010023367, -0.010276617, -0.023019087, 0.0068244194, -0.0012520878, -0.0015086699, 0.022046074, -0.034148756, -0.0022192693, 0.002427534, -0.0027124402, 0.0060346797, 0.015461575, 0.0137554705, 0.009230294, -0.009583511, 0.032629255, 0.015994733, -0.019167023, -0.009203636, 0.03393549, -0.017274313, -0.012042701, -0.0009930064, 0.026777849, -0.013582194, -0.0027590916, -0.017594207, -0.026804507, -0.0014236979, -0.022032745, 0.0091236625, -0.0042419364, -0.00858384, -0.0033905501, -0.020739838, 0.016821127, 0.022539245, 0.015381602, 0.015141681, 0.028817179, -0.019726837, -0.0051283115, -0.011489551, -0.013208984, -0.0047017853, -0.0072309524, 0.01767418, 0.0025658219, -0.010323267, 0.012609182, -0.028097415, 0.026871152, -0.010276617, 0.021912785, 0.0022542577, 0.005124979, -0.0019710176, 0.004518512, -0.040360045, 0.010969722, -0.0031539614, -0.020366628, -0.025778178, -0.0110030435, -0.016221326, 0.0036587953, 0.016207997, 0.003007343, -0.0032555948, 0.0044052163, -0.022046074, -0.0008822095, -0.009363583, 0.028230704, -0.024538586, 0.0029840174, 0.0016044717, -0.014181997, 0.031349678, -0.014381931, -0.027750863, 0.02613806, 0.0004136138, -0.005748107, -0.01868718, -0.0010138329, 0.0054348772, 0.010703143, -0.003682121, 0.0030856507, -0.004275259, -0.010403241, 0.021113047, -0.022685863, -0.023032416, 0.031429652, 0.001792743, -0.005644808, -0.011842767, -0.04078657, -0.0026874484, 0.06915057, -0.00056939584, -0.013995391, 0.010703143, -0.013728813, -0.022939114, -0.015261642, -0.022485929, 0.016807798, 0.007964044, 0.0144219175, 0.016821127, 0.0076241563, 0.005461535, -0.013248971, 0.015301628, 0.0085171955, -0.004318578, 0.011136333, -0.0059047225, -0.010249958, -0.018207338, 0.024645219, 0.021752838, 0.0007614159, -0.013648839, 0.01111634, -0.010503208, -0.0038487327, -0.008203966, -0.00397869, 0.0029740208, 0.008530525, 0.005261601, 0.01642126, -0.0038753906, -0.013222313, 0.026537929, 0.024671877, -0.043505676, 0.014195326, 0.024778508, 0.0056914594, -0.025951454, 0.017620865, -0.0021359634, 0.008643821, 0.021299653, 0.0041686273, -0.009017031, 0.04044002, 0.024378639, -0.027777521, -0.014208655, 0.0028623908, 0.042119466, 0.005801423, -0.028124074, -0.03129636, 0.022139376, -0.022179363, -0.04067994, 0.013688826, 0.013328944, 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    26    };
    27
    28    // Define the pipeline with vectorSearch query options
    29    mongocxx::pipeline stages;
    30
    31    stages
    32        .append_stage(make_document(
    33            kvp("$vectorSearch",
    34                make_document(kvp("index", "vector_index"),
    35                              kvp("path", "plot_embedding"),
    36                              kvp("queryVector", vectors),
    37                              kvp("numCandidates", 150), kvp("limit", 10)))))
    38        .project(make_document(
    39            kvp("_id", 0), kvp("plot", 1), kvp("title", 1),
    40            kvp("score", make_document(kvp("$meta", "vectorSearchScore")))));
    41
    42    auto cursor = collection.aggregate(stages);
    43
    44    for (auto&& doc : cursor) {
    45      std::cout << bsoncxx::to_json(doc) << std::endl;
    46    }
    47  } catch (const std::exception& e) {
    48    std::cout << "Exception: " << e.what() << std::endl;
    49  }
    50  return 0;
    51}

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

3

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
4

クエリをコンパイルして実行します。

  1. /buildディレクトリを作成して入力します。

    mkdir build && cd build

  2. プロジェクトを準備します。

    cmake ../

  3. アプリをビルドします。

    cmake --build .

  4. アプリを実行してクエリを実行します。

    ./query_quick_start
    1'{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
    2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
    3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
    4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
    5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
    6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
    7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
    8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
    9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
    10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'
1

クエリの .NET/C# プロジェクトを設定して初期化します。

  1. query-quick-start という新しいディレクトリを作成し、dotnet new コマンドでプロジェクトを初期化します。

    mkdir query-quick-start
    cd query-quick-start
    dotnet new console

  2. .NET/C# 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。

    dotnet add package MongoDB.Driver

インストール手順の詳細については、 MongoDB C# ドライバーのドキュメント を参照してください。

2

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. Program.csファイルを編集します。

  2. 次のサンプル クエリをコピーしてProgram.csファイルに貼り付けます。

    1using MongoDB.Bson;
    2using MongoDB.Bson.Serialization.Attributes;
    3using MongoDB.Bson.Serialization.Conventions;
    4using MongoDB.Driver;
    5
    6public class vectorSearchBasicQuery 
    7{
    8  // define connection to your Atlas cluster
    9  private const string MongoConnectionString = "<connection-string>";
    10
    11  public static void Main(string[] args){
    12    var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
    13    ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
    14
    15    // connect to your Atlas cluster
    16    var mongoClient = new MongoClient(MongoConnectionString);
    17
    18    // define namespace
    19    var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
    20    var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
    21
    22    // define vector embeddings to search
    23    var vector = new[] 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    24    var options = new VectorSearchOptions<EmbeddedMovie>() {
    25        IndexName = "vector_index",
    26        NumberOfCandidates = 150
    27    };
    28
    29    // run query
    30    var results = moviesCollection.Aggregate()
    31                .VectorSearch(movie => movie.Embedding, vector, 10, options)
    32                .Project(Builders<EmbeddedMovie>.Projection
    33                  .Include(movie => movie.Title)
    34                  .Include(movie => movie.Plot)
    35                  .Exclude(movie => movie.Id)
    36                  .MetaVectorSearchScore(movie => movie.Score))
    37                .ToList();
    38
    39    // print results
    40    foreach (var movie in results)
    41      {
    42        Console.WriteLine(movie.ToJson());
    43      }
    44  }
    45}
    46
    47[BsonIgnoreExtraElements]
    48public class EmbeddedMovie
    49{
    50    [BsonIgnoreIfDefault]
    51    public ObjectId Id { get; set; }
    52    public string Title { get; set; }
    53    public string Plot { get; set; }
    54    [BsonElement("plot_embedding")]
    55    public double[] Embedding { get; set; }
    56    public double Score { get; set; }
    57}

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

3

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
4

クエリを実行します。

Program.csファイルをコンパイルして実行します。

dotnet run query-quick-start.csproj
1{ "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : 0.78926712274551392 }
2{ "plot" : "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title" : "About Time", "score" : 0.78436046838760376 }
3{ "plot" : "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title" : "The Time Machine", "score" : 0.78010666370391846 }
4{ "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : 0.77891707420349121 }
5{ "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : 0.77716124057769775 }
6{ "plot" : "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title" : "A.P.E.X.", "score" : 0.77308857440948486 }
7{ "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : 0.77123808860778809 }
8{ "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland", "score" : 0.76699239015579224 }
9{ "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : 0.76493728160858154 }
10{ "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal", "score" : 0.76407861709594727 }
1

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. atlas_vector_search_quick_start.goという名前のファイルを作成します。

  2. 次のサンプル クエリをコピーしてatlas_vector_search_quick_start.goファイルに貼り付けます。

    1package main
    2
    3import (
    4	"context"
    5	"encoding/json"
    6	"fmt"
    7	"log"
    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 = "<connection-string>"
    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
    31	// Define the pipeline with $vectorSearch query options
    32	queryVectorValues := []float64{
    33		-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 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0.014541878, -0.015701497, -0.029350337, 0.021979429, 0.01332228, -0.026244693, -0.0123492675, -0.003895384, 0.0071576433, -0.035454992, -0.00046984528, 0.0033522295, 0.039347045, 0.0005119148, 0.00476843, -0.012995721, 0.0024042083, -0.006931051, -0.014461905, -0.0127558, 0.0034555288, -0.0074842023, -0.030256703, -0.007057676, -0.00807734, 0.007804097, -0.006957709, 0.017181009, -0.034575284, -0.008603834, -0.005008351, -0.015834786, 0.02943031, 0.016861115, -0.0050849924, 0.014235313, 0.0051449724, 0.0025924798, -0.0025741523, 0.04289254, -0.002104307, 0.012969063, -0.008310596, 0.00423194, 0.0074975314, 0.0018810473, -0.014248641, -0.024725191, 0.0151016945, -0.017527562, 0.0018727167, 0.0002830318, 0.015168339, 0.0144219175, -0.004048667, -0.004358565, 0.011836103, -0.010343261, -0.005911387, 0.0022825818, 0.0073175905, 0.00403867, 0.013188991, 0.03334902, 0.006111321, 0.008597169, 0.030123414, -0.015474904, 0.0017877447, -0.024551915, 0.013155668, 0.023525586, -0.0255116, 0.017220996, 0.004358565, -0.00934359, 0.0099967085, 0.011162991, 0.03092315, -0.021046404, -0.015514892, 0.0011946067, -0.01816735, 0.010876419, -0.10124666, -0.03550831, 0.0056348112, 0.013942076, 0.005951374, 0.020419942, -0.006857742, -0.020873128, -0.021259667, 0.0137554705, 0.0057880944, -0.029163731, -0.018767154, -0.021392956, 0.030896494, -0.005494857, -0.0027307675, -0.006801094, -0.014821786, 0.021392956, -0.0018110704, -0.0018843795, -0.012362596, -0.0072176233, -0.017194338, -0.018713837, -0.024272008, 0.03801415, 0.00015880188, 0.0044951867, -0.028630573, -0.0014070367, -0.00916365, -0.026537929, -0.009576847, -0.013995391, -0.0077107945, 0.0050016865, 0.00578143, -0.04467862, 0.008363913, 0.010136662, -0.0006268769, -0.006591163, 0.015341615, -0.027377652, -0.00093136, 0.029243704, -0.020886457, -0.01041657, -0.02424535, 0.005291591, -0.02980352, -0.009190307, 0.019460259, -0.0041286405, 0.004801752, 0.0011787785, -0.001257086, -0.011216307, -0.013395589, 0.00088137644, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136,
    34	}
    35
    36	pipeline := mongo.Pipeline{
    37		bson.D{
    38			{"$vectorSearch", bson.D{
    39				{"queryVector", queryVectorValues},
    40				{"index", "vector_index"},
    41				{"path", "plot_embedding"},
    42				{"numCandidates", 150},
    43				{"limit", 10},
    44			}},
    45		},
    46		bson.D{
    47			{"$project", bson.D{
    48				{"_id", 0},
    49				{"plot", 1},
    50				{"title", 1},
    51				{"score", bson.D{
    52					{"$meta", "vectorSearchScore"},
    53				}},
    54			}},
    55		},
    56	}
    57
    58	// Run the pipeline
    59	cursor, err := coll.Aggregate(ctx, pipeline)
    60	if err != nil {
    61		log.Fatalf("Failed to run aggregation: %v", err)
    62	}
    63	defer cursor.Close(ctx)
    64
    65	// Print results
    66	for cursor.Next(ctx) {
    67		var doc bson.M
    68		if err := cursor.Decode(&doc); err != nil {
    69			log.Fatalf("Failed to decode document: %v", err)
    70		}
    71		jsonDoc, err := json.Marshal(doc)
    72		if err != nil {
    73			log.Fatalf("Failed to marshal document to JSON: %v", err)
    74		}
    75		fmt.Println(string(jsonDoc))
    76	}
    77
    78	if err := cursor.Err(); err != nil {
    79		log.Fatalf("Cursor error: %v", err)
    80	}
    81}

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

2

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
3

クエリを実行します。

次のコマンドを実行して、コレクションをクエリします。

go run atlas_vector_search_quick_start.go
1'{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'
1

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. BasicQuery.javaという名前のファイルを作成します。

  2. 次のサンプル クエリをコピーしてBasicQuery.javaファイルに貼り付けます。

    1import com.mongodb.client.MongoClient;
    2import com.mongodb.client.MongoClients;
    3import com.mongodb.client.MongoCollection;
    4import com.mongodb.client.MongoDatabase;
    5import com.mongodb.client.model.search.FieldSearchPath;
    6import org.bson.Document;
    7import org.bson.conversions.Bson;
    8
    9import java.util.List;
    10
    11import static com.mongodb.client.model.Aggregates.project;
    12import static com.mongodb.client.model.Aggregates.vectorSearch;
    13import static com.mongodb.client.model.Projections.fields;
    14import static com.mongodb.client.model.Projections.include;
    15import static com.mongodb.client.model.Projections.exclude;
    16import static com.mongodb.client.model.Projections.metaVectorSearchScore;
    17import static com.mongodb.client.model.search.SearchPath.fieldPath;
    18import static com.mongodb.client.model.search.VectorSearchOptions.approximateVectorSearchOptions;
    19import static java.util.Arrays.asList;
    20
    21public class BasicQuery {
    22  public static void main( String[] args ) {
    23    // specify connection
    24    String uri = "<connection-string>";
    25
    26    // establish connection and set namespace
    27    try (MongoClient mongoClient = MongoClients.create(uri)) {
    28      MongoDatabase database = mongoClient.getDatabase("sample_mflix");
    29      MongoCollection<Document> collection = database.getCollection("embedded_movies");
    30      
    31
    32      // define $vectorSearch query options
    33      List<Double> queryVector = (asList(-0.0016261312d, -0.028070757d, -0.011342932d, -0.012775794d, -0.0027440966d, 0.008683807d, -0.02575152d, -0.02020668d, -0.010283281d, -0.0041719596d, 0.021392956d, 0.028657231d, -0.006634482d, 0.007490867d, 0.018593878d, 0.0038187427d, 0.029590257d, -0.01451522d, 0.016061379d, 0.00008528442d, -0.008943722d, 0.01627464d, 0.024311995d, -0.025911469d, 0.00022596726d, -0.008863748d, 0.008823762d, -0.034921836d, 0.007910728d, -0.01515501d, 0.035801545d, -0.0035688248d, -0.020299982d, -0.03145631d, -0.032256044d, -0.028763862d, -0.0071576433d, -0.012769129d, 0.012322609d, -0.006621153d, 0.010583182d, 0.024085402d, -0.001623632d, 0.007864078d, -0.021406285d, 0.002554159d, 0.012229307d, -0.011762793d, 0.0051682983d, 0.0048484034d, 0.018087378d, 0.024325324d, -0.037694257d, -0.026537929d, -0.008803768d, -0.017767483d, -0.012642504d, -0.0062712682d, 0.0009771782d, -0.010409906d, 0.017754154d, -0.004671795d, -0.030469967d, 0.008477209d, -0.005218282d, -0.0058480743d, -0.020153364d, -0.0032805866d, 0.004248601d, 0.0051449724d, 0.006791097d, 0.007650814d, 0.003458861d, -0.0031223053d, -0.01932697d, -0.033615597d, 0.00745088d, 0.006321252d, -0.0038154104d, 0.014555207d, 0.027697546d, -0.02828402d, 0.0066711367d, 0.0077107945d, 0.01794076d, 0.011349596d, -0.0052715978d, 0.014755142d, -0.019753495d, -0.011156326d, 0.011202978d, 0.022126047d, 0.00846388d, 0.030549942d, -0.0041386373d, 0.018847128d, -0.00033655585d, 0.024925126d, -0.003555496d, -0.019300312d, 0.010749794d, 0.0075308536d, -0.018287312d, -0.016567878d, -0.012869096d, -0.015528221d, 0.0078107617d, -0.011156326d, 0.013522214d, -0.020646535d, -0.01211601d, 0.055928253d, 0.011596181d, -0.017247654d, 0.0005939711d, -0.026977783d, -0.003942035d, -0.009583511d, -0.0055248477d, -0.028737204d, 0.023179034d, 0.003995351d, 0.0219661d, -0.008470545d, 0.023392297d, 0.010469886d, -0.015874773d, 0.007890735d, -0.009690142d, -0.00024970944d, 0.012775794d, 0.0114762215d, 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    34      String indexName = "vector_index";
    35      FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
    36      int limit = 10;
    37      int numCandidates = 150;
    38      
    39      // define pipeline 
    40      List<Bson> pipeline = asList(
    41        vectorSearch(
    42          fieldSearchPath,
    43          queryVector,
    44          indexName,
    45          limit,
    46          approximateVectorSearchOptions(numCandidates)),
    47        project(
    48          fields(exclude("_id"), include("title"), include("plot"),
    49          metaVectorSearchScore("score"))));
    50      
    51      // run query and print results
    52      collection.aggregate(pipeline)
    53        .forEach(doc -> System.out.println(doc.toJson()));	
    54    }
    55  }
    56}

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

2

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
3

クエリを実行します。

BasicQuery.javaファイルをコンパイルして実行します。

javac BasicQuery.java
java BasicQuery
1{"plot": "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title": "Thrill Seekers", "score": 0.7892671227455139}
2{"plot": "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title": "About Time", "score": 0.7843604683876038}
3{"plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title": "The Time Machine", "score": 0.7801066637039185}
4{"plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title": "Crusade in Jeans", "score": 0.7789170742034912}
5{"plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title": "Timecop", "score": 0.7771612405776978}
6{"plot": "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title": "A.P.E.X.", "score": 0.7730885744094849}
7{"plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title": "Men in Black 3", "score": 0.7712380886077881}
8{"plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title": "Tomorrowland", "score": 0.7669923901557922}
9{"plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title": "Love Story 2050", "score": 0.7649372816085815}
10{"plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title": "The Portal", "score": 0.7640786170959473}
1

MongoDB Kotlin コルーチン ドライバーをインストールします。

Kotlin コルーチンドライバーのバージョン 5.2 以降を依存関係としてプロジェクトに追加します。

  • Gradle を使用している場合は、次の依存関係を

    build.gradle 依存関係リストに追加します。

    dependencies {
       implementation 'mongodb-driver-kotlin-coroutine:5.2.0'
    }
  • Maven を使用している場合は、次の依存関係を

    pom.xml 依存関係リストに追加します。

    <dependency>
       <groupId>org.mongodb</groupId>
       <artifactId>mongodb-driver-kotlin-coroutine</artifactId>
       <version>5.2.0</version>
    </dependency>

より詳細なインストール手順とバージョンの互換性については、「MongoDB Kotlin コルーチン ドライバーのドキュメント」を参照してください。

2

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. atlasVectorSearchQuery.ktという名前のファイルを作成します。

  2. 次のサンプル クエリをコピーしてatlasVectorSearchQuery.ktファイルに貼り付けます。

    1import com.mongodb.client.model.Aggregates
    2import com.mongodb.client.model.Projections
    3import com.mongodb.client.model.search.SearchPath.fieldPath
    4import com.mongodb.client.model.search.VectorSearchOptions
    5import com.mongodb.kotlin.client.coroutine.MongoClient
    6import kotlinx.coroutines.flow.count
    7import org.bson.Document
    8
    9data class Movie(val title: String, val plot: String, val score: Double?)
    10
    11suspend fun main() {
    12
    13    // Replace the placeholder with your Atlas connection string
    14    val uri = "<connection-string>"
    15    val client = MongoClient.create(uri)
    16
    17    try {
    18        // Connect to your Atlas cluster
    19        val database = client.getDatabase("sample_mflix")
    20        val collection = database.getCollection<Movie>("embedded_movies")
    21
    22        // Define the pipeline with $vectorSearch query options
    23        val queryVector = listOf(
    24            -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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0.017220996, 0.004358565, -0.00934359, 0.0099967085, 0.011162991, 0.03092315, -0.021046404, -0.015514892, 0.0011946067, -0.01816735, 0.010876419, -0.10124666, -0.03550831, 0.0056348112, 0.013942076, 0.005951374, 0.020419942, -0.006857742, -0.020873128, -0.021259667, 0.0137554705, 0.0057880944, -0.029163731, -0.018767154, -0.021392956, 0.030896494, -0.005494857, -0.0027307675, -0.006801094, -0.014821786, 0.021392956, -0.0018110704, -0.0018843795, -0.012362596, -0.0072176233, -0.017194338, -0.018713837, -0.024272008, 0.03801415, 0.00015880188, 0.0044951867, -0.028630573, -0.0014070367, -0.00916365, -0.026537929, -0.009576847, -0.013995391, -0.0077107945, 0.0050016865, 0.00578143, -0.04467862, 0.008363913, 0.010136662, -0.0006268769, -0.006591163, 0.015341615, -0.027377652, -0.00093136, 0.029243704, -0.020886457, -0.01041657, -0.02424535, 0.005291591, -0.02980352, -0.009190307, 0.019460259, -0.0041286405, 0.004801752, 0.0011787785, -0.001257086, -0.011216307, -0.013395589, 0.00088137644, 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-0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136,
    25            )
    26        val path = fieldPath("plot_embedding")
    27        val index = "vector_index"
    28        val limit = 10L
    29        val numCandidates = 150L
    30
    31        val pipeline = listOf(
    32            Aggregates.vectorSearch(
    33                path,
    34                queryVector,
    35                index,
    36                limit,
    37                VectorSearchOptions.approximateVectorSearchOptions(numCandidates)
    38            ),
    39            Aggregates.project(
    40                Projections.fields(
    41                    Projections.excludeId(),
    42                    Projections.include(Movie::plot.name),
    43                    Projections.include(Movie::title.name),
    44                    Projections.metaVectorSearchScore(Movie::score.name)
    45                )
    46            )
    47        )
    48
    49        // Run the pipeline
    50        val resultsFlow = collection.aggregate<Document>(pipeline)
    51
    52        // Print results
    53        val resultCount = resultsFlow.count()
    54        if (resultCount > 0) {
    55            resultsFlow.collect { result ->
    56                println(result.toJson())
    57            }
    58        } else {
    59            println("No results found.")
    60        }
    61    } catch (e: Exception) {
    62        println("Error occurred: ${e.message}")
    63    } finally {
    64        client.close()
    65    }
    66}

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

3

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
4

クエリを実行します。

IDE でatlasVectorSearchQuery.ktファイルを実行します。 出力は、次のようになります。

1'{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'
1

MongoDB Kotlin Sync ドライバーをインストールします。

Kotlin Sync ドライバーのバージョン 5.2 以降を依存関係としてプロジェクトに追加します。

  • Gradle を使用している場合は、次の依存関係を

    build.gradle 依存関係リストに追加します。

    dependencies {
       implementation 'mongodb-driver-kotlin-sync:5.2.0'
    }
  • Maven を使用している場合は、次の依存関係を

    pom.xml 依存関係リストに追加します。

    <dependency>
       <groupId>org.mongodb</groupId>
       <artifactId>mongodb-driver-kotlin-sync</artifactId>
       <version>5.2.0</version>
    </dependency>

より詳細なインストール手順とバージョンの互換性については、「MongoDB Kotlin Sync ドライバーのドキュメント」を参照してください。

2

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. atlasVectorSearchQuery.ktという名前のファイルを作成します。

  2. 次のサンプル クエリをコピーしてatlasVectorSearchQuery.ktファイルに貼り付けます。

    1import com.mongodb.client.model.Aggregates
    2import com.mongodb.client.model.Projections
    3import com.mongodb.client.model.search.SearchPath.fieldPath
    4import com.mongodb.client.model.search.VectorSearchOptions
    5import com.mongodb.kotlin.client.MongoClient
    6import org.bson.Document
    7
    8data class Movie(val title: String, val plot: String, val score: Double?)
    9
    10fun main() {
    11
    12    // Replace the placeholder with your Atlas connection string
    13    val uri = "<connection-string>"
    14    val client = MongoClient.create(uri)
    15
    16    try {
    17        // Connect to your Atlas cluster
    18        val database = client.getDatabase("sample_mflix")
    19        val collection = database.getCollection<Movie>("embedded_movies")
    20
    21        // Define the pipeline with $vectorSearch query options
    22        val queryVector = listOf(
    23            -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136,
    24            )
    25        val path = fieldPath("plot_embedding")
    26        val index = "vector_index"
    27        val limit = 10L
    28        val numCandidates = 150L
    29
    30        val pipeline = listOf(
    31            Aggregates.vectorSearch(
    32                path,
    33                queryVector,
    34                index,
    35                limit,
    36                VectorSearchOptions.approximateVectorSearchOptions(numCandidates)
    37            ),
    38            Aggregates.project(
    39                Projections.fields(
    40                    Projections.excludeId(),
    41                    Projections.include(Movie::plot.name),
    42                    Projections.include(Movie::title.name),
    43                    Projections.metaVectorSearchScore(Movie::score.name)
    44                )
    45            )
    46        )
    47
    48        // Run the pipeline
    49        val results = collection.aggregate<Document>(pipeline).toList()
    50
    51        // Print results
    52        if (results.isNotEmpty()) {
    53            results.forEach { result ->
    54                println(result.toJson()).toString()
    55            }
    56        } else {
    57            println("No results found.")
    58        }
    59    } catch (e: Exception) {
    60        println("Error occurred: ${e.message}")
    61    } finally {
    62        client.close()
    63    }
    64}

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

3

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
4

クエリを実行します。

IDE でatlasVectorSearchQuery.ktファイルを実行します。 出力は、次のようになります。

1'{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'
1

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. atlas-vector-search-quick-start.jsという名前のファイルを作成します。

  2. 次のサンプル クエリをコピーしてatlas-vector-search-quick-start.jsファイルに貼り付けます。

    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              '$vectorSearch': {
    20                'index': 'vector_index',
    21                'path': 'plot_embedding',
    22                'queryVector': [
    23                  -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136
    24                ],
    25                'numCandidates': 150,
    26                'limit': 10
    27              }
    28            }, {
    29              '$project': {
    30                '_id': 0,
    31                'plot': 1,
    32                'title': 1,
    33                'score': {
    34                  '$meta': 'vectorSearchScore'
    35                }
    36              }
    37            }
    38          ];
    39        // run pipeline
    40        const result = coll.aggregate(agg);
    41
    42        // print results
    43        await result.forEach((doc) => console.dir(JSON.stringify(doc)));
    44    } finally {
    45        await client.close();
    46    }
    47}
    48run().catch(console.dir);

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

2

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
3

クエリを実行します。

次のコマンドを実行して、コレクションをクエリします。

node atlas-vector-search-quick-start.js
1'{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'
1

MongoDB PHP ドライバーをインストールします。

インストール手順の詳細については、 MongoDB PHP ライブラリのドキュメント を参照してください。

2

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. atlas_vector_search_quick_start.phpという名前のファイルを作成します。

  2. 次のサンプル クエリをコピーしてatlas_vector_search_quick_start.phpファイルに貼り付けます。

    1<?php
    2
    3require 'vendor/autoload.php';
    4
    5// Replace the placeholder with your Atlas connection string
    6$uri = '<connection-string>';
    7
    8try {
    9
    10    // Connect to your Atlas cluster
    11    $client = new MongoDB\Client($uri);
    12
    13    // Set the namespace
    14    $database = $client->selectDatabase('sample_mflix');
    15    $collection = $database->selectCollection('embedded_movies');
    16
    17    // Define the pipeline with $vectorSearch query options
    18    $pipeline = [
    19        [
    20            '$vectorSearch' => [
    21                'queryVector' => [
    22                    -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136
    23                ],
    24                'index' => 'vector_index',
    25                'path' => 'plot_embedding',
    26                'numCandidates' => 150,
    27                'limit' => 10
    28            ]
    29        ],
    30        [
    31            '$project' => [
    32                '_id' => 0,
    33                'plot' => 1,
    34                'title' => 1,
    35                'score' => [
    36                    '$meta' => 'vectorSearchScore'
    37                ]
    38            ]
    39        ]
    40    ];
    41
    42    // Run the pipeline
    43    $result = $collection->aggregate($pipeline);
    44
    45    // Print results
    46    foreach ($result as $document) {
    47        echo json_encode($document) . "\n";
    48    }
    49
    50} catch (MongoDB\Driver\Exception\Exception $e) {
    51    echo "Error: " . $e->getMessage() . "\n";
    52}

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

3

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
4

クエリを実行します。

次のコマンドを実行して、コレクションをクエリします。

php atlas_vector_search_quick_start.php
1'{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'
1

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. atlas-vector-search-quick-start.pyという名前のファイルを作成します。

  2. 次のサンプル クエリをコピーしてatlas-vector-search-quick-start.pyファイルに貼り付けます。

    1import pymongo
    2import datetime
    3
    4# connect to your Atlas cluster
    5client = pymongo.MongoClient("<connection-string>")
    6
    7# define pipeline
    8pipeline = [
    9  {
    10    '$vectorSearch': {
    11      'index': 'vector_index', 
    12      'path': 'plot_embedding', 
    13      'queryVector': [-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, -0.020473259, 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-0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136], 
    14      'numCandidates': 150, 
    15      'limit': 10
    16    }
    17  }, {
    18    '$project': {
    19      '_id': 0, 
    20      'plot': 1, 
    21      'title': 1, 
    22      'score': {
    23        '$meta': 'vectorSearchScore'
    24      }
    25    }
    26  }
    27]
    28
    29# run pipeline
    30result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
    31
    32# print results
    33for i in result:
    34    print(i)
    35

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

2

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
3

クエリを実行します。

次のコマンドを実行して、コレクションをクエリします。

python atlas-vector-search-quick-start.py
1{'plot': 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', 'title': 'Thrill Seekers', 'score': 0.7892671227455139}
2{'plot': 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.', 'title': 'About Time', 'score': 0.7843604683876038}
3{'plot': 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', 'title': 'The Time Machine', 'score': 0.7801066637039185}
4{'plot': "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", 'title': 'Crusade in Jeans', 'score': 0.7789170742034912}
5{'plot': 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', 'title': 'Timecop', 'score': 0.7771612405776978}
6{'plot': 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', 'title': 'A.P.E.X.', 'score': 0.7730885744094849}
7{'plot': "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 'title': 'Men in Black 3', 'score': 0.7712380886077881}
8{'plot': 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 'title': 'Tomorrowland', 'score': 0.7669923901557922}
9{'plot': 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 'title': 'Love Story 2050', 'score': 0.7649372816085815}
10{'plot': 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 'title': 'The Portal', 'score': 0.7640786170959473}
1

MongoDB Ruby ドライバーをインストールします。

  1. プロジェクトにGemfileがまだない場合は、次のコマンドを実行して を生成します。

    bundle init

  2. 以下のように、 mongo gem をGemfileに追加します。

    Gemfile
    gem "mongo"

  3. 次のコマンドを実行して、依存関係をインストールします。

    bundle install

これにより、Ruby ドライバーの最新バージョンがインストールされます。 代替のインストール手順とバージョンの互換性については、 MongoDB Ruby ドライバーのドキュメント を参照してください。

2

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. atlas_vector_search_quick_start.rbという名前のファイルを作成します。

  2. 次のサンプル クエリをコピーしてatlas_vector_search_quick_start.rbファイルに貼り付けます。

    1require 'mongo'
    2require 'json'
    3
    4# Replace the placeholder with your Atlas connection string
    5uri = "<connection-string>"
    6
    7begin
    8  # Connect to the MongoDB client
    9  client = Mongo::Client.new(uri, database: 'sample_mflix')
    10
    11  # Set the namespace
    12  db = client.database
    13  collection = db[:embedded_movies]
    14
    15  # Define the pipeline with $vectorSearch query options
    16  pipeline = [
    17    {
    18      '$vectorSearch' => {
    19        'queryVector' => [ -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 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-0.006684466, 0.01306903, 0.00015765642, -0.00530492, 0.0005277429, 0.015421589, 0.015528221, 0.032202728, -0.003485519, -0.0014286962, 0.033908837, 0.001367883, 0.010509873, 0.025271678, -0.020993087, 0.019846799, 0.006897729, -0.010216636, -0.00725761, 0.01818068, -0.028443968, -0.011242964, -0.014435247, -0.013688826, 0.006101324, -0.0022509254, 0.013848773, -0.0019077052, 0.017181009, 0.03422873, 0.005324913, -0.0035188415, 0.014128681, -0.004898387, 0.005038341, 0.0012320944, -0.005561502, -0.017847456, 0.0008538855, -0.0047884234, 0.011849431, 0.015421589, -0.013942076, 0.0029790192, -0.013702155, 0.0001199605, -0.024431955, 0.019926772, 0.022179363, -0.016487904, -0.03964028, 0.0050849924, 0.017487574, 0.022792496, 0.0012504216, 0.004048667, -0.00997005, 0.0076041627, -0.014328616, -0.020259995, 0.0005598157, -0.010469886, 0.0016852784, 0.01716768, -0.008990373, -0.001987679, 0.026417969, 0.023792166, 0.0046917885, -0.0071909656, -0.00032051947, -0.023259008, -0.009170313, 0.02071318, -0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136 ],
    20        'index' => 'vector_index',
    21        'path' => 'plot_embedding',
    22        'numCandidates' => 150,
    23        'limit' => 10
    24      }
    25    },
    26    {
    27      '$project' => {
    28        '_id' => 0,
    29        'plot' => 1,
    30        'title' => 1,
    31        'score' => { '$meta' => 'vectorSearchScore' }
    32      }
    33    }
    34  ]
    35
    36  # Run the pipeline
    37  results = collection.aggregate(pipeline)
    38
    39  # Print results
    40  results.each do |document|
    41    puts JSON(document)
    42  end
    43
    44rescue Mongo::Error::NoServerAvailable => e
    45  puts "Failed to connect to MongoDB: #{e.message}"
    46
    47rescue Mongo::Error::OperationFailure => e
    48  puts "Failed to run aggregation: #{e.message}"
    49
    50rescue Mongo::Error => e
    51  puts "A MongoDB error occurred: #{e.message}"
    52
    53rescue StandardError => e
    54  puts "A general error occurred: #{e.message}"
    55
    56ensure
    57  client&.close
    58  puts "MongoDB client closed."
    59end

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

3

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
4

クエリを実行します。

次のコマンドを実行して、コレクションをクエリします。

bundle exec ruby atlas_vector_search_quick_start.rb
1'{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'
1

MongoDB 用の Rust ドライバーをインストールします。

インストール手順の詳細については、 MongoDB Rust ドライバーのドキュメント を参照してください。

2

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

プロジェクトのmain.rsファイルに、次のコードを貼り付けます。

#![recursion_limit = "2560"]
use mongodb::{ 
    bson::{Document, doc},
    Client
};
use futures::TryStreamExt;
#[tokio::main]
async fn main() -> mongodb::error::Result<()> {
    // Replace the placeholder with your Atlas connection string
    let client = Client::with_uri_str("<connection-string>").await?;
    
    let pipeline  = vec! [
        doc! {
            "$vectorSearch": doc! {
            "queryVector": [ -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, -0.020473259, 0.004671795, -0.012555866, 0.0209531, 0.01982014, 0.024485271, 0.0105431955, -0.005178295, 0.033162415, -0.013795458, 0.007150979, 0.010243294, 0.005644808, 0.017260984, -0.0045618312, 0.0024725192, 0.004305249, -0.008197301, 0.0014203656, 0.0018460588, 0.005015015, -0.011142998, 0.01439526, 0.022965772, 0.02552493, 0.007757446, -0.0019726837, 0.009503538, -0.032042783, 0.008403899, -0.04609149, 0.013808787, 0.011749465, 0.036388017, 0.016314628, 0.021939443, -0.0250051, -0.017354285, -0.012962398, 0.00006107364, 0.019113706, 0.03081652, -0.018114036, -0.0084572155, 0.009643491, -0.0034721901, 0.0072642746, -0.0090636825, 0.01642126, 0.013428912, 0.027724205, 0.0071243206, -0.6858542, -0.031029783, -0.014595194, -0.011449563, 0.017514233, 0.01743426, 0.009950057, 0.0029706885, -0.015714826, -0.001806072, 0.011856096, 0.026444625, -0.0010663156, -0.006474535, 0.0016161345, -0.020313311, 0.0148351155, -0.0018393943, 0.0057347785, 0.018300641, -0.018647194, 0.03345565, -0.008070676, 0.0071443142, 0.014301958, 0.0044818576, 0.003838736, -0.007350913, -0.024525259, -0.001142124, -0.018620536, 0.017247654, 0.007037683, 0.010236629, 0.06046009, 0.0138887605, -0.012122675, 0.037694257, 0.0055081863, 0.042492677, 0.00021784494, -0.011656162, 0.010276617, 0.022325981, 0.005984696, -0.009496873, 0.013382261, -0.0010563189, 0.0026507939, -0.041639622, 0.008637156, 0.026471283, -0.008403899, 0.024858482, -0.00066686375, -0.0016252982, 0.027590916, 0.0051449724, 0.0058647357, -0.008743787, -0.014968405, 0.027724205, -0.011596181, 0.0047650975, -0.015381602, 0.0043718936, 0.002159289, 0.035908177, -0.008243952, -0.030443309, 0.027564257, 0.042625964, -0.0033688906, 0.01843393, 0.019087048, 0.024578573, 0.03268257, -0.015608194, -0.014128681, -0.0033538956, -0.0028757197, -0.004121976, -0.032389335, 0.0034322033, 0.058807302, 0.010943064, -0.030523283, 0.008903735, 0.017500903, 0.00871713, -0.0029406983, 0.013995391, -0.03132302, -0.019660193, -0.00770413, -0.0038853872, 0.0015894766, -0.0015294964, -0.006251275, -0.021099718, -0.010256623, -0.008863748, 0.028550599, 0.02020668, -0.0012962399, -0.003415542, -0.0022509254, 0.0119360695, 0.027590916, -0.046971202, -0.0015194997, -0.022405956, 0.0016677842, -0.00018535563, -0.015421589, -0.031802863, 0.03814744, 0.0065411795, 0.016567878, -0.015621523, 0.022899127, -0.011076353, 0.02841731, -0.002679118, -0.002342562, 0.015341615, 0.01804739, -0.020566562, -0.012989056, -0.002990682, 0.01643459, 0.00042527664, 0.008243952, -0.013715484, -0.004835075, -0.009803439, 0.03129636, -0.021432944, 0.0012087687, -0.015741484, -0.0052016205, 0.00080890034, -0.01755422, 0.004811749, -0.017967418, -0.026684547, -0.014128681, 0.0041386373, -0.013742141, -0.010056688, -0.013268964, -0.0110630235, -0.028337335, 0.015981404, -0.00997005, -0.02424535, -0.013968734, -0.028310679, -0.027750863, -0.020699851, 0.02235264, 0.001057985, 0.00081639783, -0.0099367285, 0.013522214, -0.012016043, -0.00086471526, 0.013568865, 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-0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136
            ],
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 10
        }
        },
        doc! {
            "$project": doc! {
                "_id": 0,
                "plot": 1,
                "title": 1,
                "score": doc! { "$meta": "vectorSearchScore"
                }
            }
        }
    ];
    let coll = client.database("sample_mflix").collection::<Document>("embedded_movies");
    let mut results = coll.aggregate(pipeline).await?;
    while let Some(result) = results.try_next().await? {
        println!("{}", result);
    }
    Ok(())
}
#![recursion_limit = "2560"]
use mongodb::{ 
    bson::{Document, doc},
    sync::Client
};
fn main() -> mongodb::error::Result<()> {
    // Replace the placeholder with your Atlas connection string
    let client = Client::with_uri_str("<connection-string>")?;
    let pipeline  = vec! [
        doc! {
            "$vectorSearch": doc! {
            "queryVector": [ -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 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            ],
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 10
        }
        },
        doc! {
            "$project": doc! {
                "_id": 0,
                "plot": 1,
                "title": 1,
                "score": doc! { "$meta": "vectorSearchScore"
                }
            }
        }
    ];
    let coll = client.database("sample_mflix").collection::<Document>("embedded_movies");
    let mut cursor = coll.aggregate(pipeline).run()?;
    while cursor.advance()? {
        println!("{}", cursor.deserialize_current()?);
    }
    Ok(())
}

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

3

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
4

クエリを実行します。

次のコマンドを実行して、コレクションをクエリします。

cargo run
1{'plot': 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', 'title': 'Thrill Seekers', 'score': 0.7892671227455139}
2{'plot': 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.', 'title': 'About Time', 'score': 0.7843604683876038}
3{'plot': 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', 'title': 'The Time Machine', 'score': 0.7801066637039185}
4{'plot': "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", 'title': 'Crusade in Jeans', 'score': 0.7789170742034912}
5{'plot': 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', 'title': 'Timecop', 'score': 0.7771612405776978}
6{'plot': 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', 'title': 'A.P.E.X.', 'score': 0.7730885744094849}
7{'plot': "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 'title': 'Men in Black 3', 'score': 0.7712380886077881}
8{'plot': 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 'title': 'Tomorrowland', 'score': 0.7669923901557922}
9{'plot': 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 'title': 'Love Story 2050', 'score': 0.7649372816085815}
10{'plot': 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 'title': 'The Portal', 'score': 0.7640786170959473}
1

MongoDB Scala ドライバーをインストールします。

環境と使用している Scala のバージョンに基づいてインストール手順について詳しくは、「 MongoDB Scala ドライバーのドキュメント 」を参照してください。

2

ベクトル検索クエリを構築します。

このクエリは、「time travel」というタームにセマンティックに関連するテキストをplotフィールドに含むドキュメントを検索します。

  1. 通常、使用するツールを使用して新しい Scala プロジェクトを作成します。 この簡単な開始のために、quick-start sdb を使用して という名前のプロジェクトを作成します 。

    sbt new scala/hello-world.g8

    プロンプトが表示されたら、アプリケーションにquick-startという名前を付けます。

  2. Main.scalaファイルを開き、内容を次のベクトル検索クエリで置き換えます。

    import org.mongodb.scala._
    import org.mongodb.scala.model._
    import org.mongodb.scala.model.Aggregates._
    import org.mongodb.scala.model.Projections._
    import org.mongodb.scala.model.search.SearchPath.fieldPath
    object Main extends App {
        val mongoClient: MongoClient = MongoClient("<connection-string>")
        val database: MongoDatabase = mongoClient.getDatabase("sample_mflix")
        val collection: MongoCollection[Document] = database.getCollection("embedded_movies")
        collection.aggregate(Seq(
            Aggregates.vectorSearch(fieldPath("plot_embedding"),
                List(-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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-0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136),
            "vector_index",
            150,
            10
            ),
            Aggregates.project(
            Projections.fields(
                Projections.excludeId(),
                Projections.include("plot"),
                Projections.include("title"),
                Projections.meta(
                "score", "vectorSearchScore"
                )
            )
        )
      )).foreach { doc => println(doc.toBsonDocument()) }
    }

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

  • sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドで、検索タームのベクトル埋め込みと映画プロットのベクトル埋め込みを比較します。

  • 最も類似した映画のプロットを最大150まで検討し、上位の10結果を返します。

$projectステージは次の目的で使用されます。

  • 結果には映画「 plot 」と「 titleフィールドのみが含まれます。

  • 検索タームと各結果の関連性を示すために、 scoreフィールドを追加します。

このパイプライン ステージの詳細については、「ベクトル検索クエリの実行 」を参照してください。

3

<connection-string>を指定します。

<connection-string>を Atlas クラスターまたはローカル配置の接続文字列に置き換え、ファイルを保存します。

接続stringには、次の形式を使用する必要があります。

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

注意

接続stringにデータベースユーザーの認証情報が含まれていることを確認します。 接続stringの検索方法について詳しくは、「ドライバー経由で接続 」を参照してください。

接続stringには、次の形式を使用する必要があります。

mongodb://localhost:<port-number>/?directConnection=true
4

クエリを実行します。

Scala が実行されるツールと環境は多数あります。 この例では、 コマンドで sbtsbt サーバーを起動し、 と入力して、新しい Scala~run プロジェクトを実行します。

sbt:quick-start> ~run
1'{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'

学習の概要

このクイック スタートでは、指定されたクエリにセマンティックに関連するテキストを含むドキュメントを Atlas クラスターから取得する方法に焦点を当てています。 ただし、画像やビデオなど、Atlas クラスターに書き込む可能性のある任意のデータを表す埋め込みにベクトル検索インデックスを作成することはできます。

サンプル データ

このクイック スタートでは、映画の詳細を含むsample_mflix.embedded_moviesコレクションを使用します。 コレクション内の各ドキュメントで、 plot_embeddingフィールドには、 plotフィールドの string を表すベクトル埋め込みが含まれています。 コレクション内のドキュメントのスキーマの詳細については、「 Mflix サンプル データセット 」を参照してください。

ソース データとそれに対応するベクトル埋め込みを同じドキュメントに保存することで、複雑なクエリやハイブリッド検索で両方のフィールドを活用できます。 同じドキュメントに異なる埋め込みモデルから生成されたベクトル埋め込みを保存して、特定のユースケースに応じてさまざまなベクトル埋め込みモデルのパフォーマンスをテストする際にワークフローを効率化することもできます。

ベクトル埋め込み

sample_mflix.embedded_moviesコレクションとサンプル クエリ内の ベクトル埋め込みは、OpenAI text-embedding-ada-002埋め込みモデルを使用して作成されました。 埋め込みモデルの選択は、ベクトル検索インデックスで使用するベクトル次元とベクトル類似度関数に影響します。 任意の埋め込みモデルを使用できますが、特定のユースケースに応じてモデルによって精度が異なる可能性があるため、さまざまなモデルを試す価値があります。

独自のデータのベクトル埋め込みを作成する方法については、「ベクトル埋め込みの作成方法 」を参照してください。

ベクトル インデックスの定義

インデックスは、特定のクエリのデータベースのパフォーマンスを向上させるコレクションのドキュメントのデータのサブセットを保持するデータ構造です。 ベクトル検索インデックスは、ベクトル埋め込みを含むフィールドを指し、ベクトルの次元、およびクエリのベクトルとデータベースに保存されているベクトル間の類似性を測定するために使用される関数が含まれます。

このクイック スタートで使用されているtext-embedding-ada-002埋め込みモデルは、データを1536次元のベクトル埋め込みに変換し、 cosine関数をサポートしているため、このベクトル検索インデックスでは同じ数のベクトル次元と類似性関数を指定します。

ベクトル検索クエリ

このクイック スタートで実行したクエリは集計パイプラインであり、 $vectorSearchステージがANN（近似最近傍）検索を実行し、その後に結果を微調整する$projectステージが続きます。 完全一致（enn）の使用や、 filterオプションを使用してベクトル検索の範囲を絞り込む方法など、ベクトル検索クエリのすべてのオプションを確認するには、「ベクトル検索クエリの実行 」を参照してください。

次のステップ

戻る

Atlas Vector Search

次へ

ベクトル埋め込みの作成方法