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Atlas クラスターのデータに対してセマンティック検索を実行する方法

項目一覧

  • 前提条件
  • Atlas Vector Search インデックスの作成
  • 必要なアクセス権
  • 手順
  • $vectorSearch集計パイプライン ステージを使用してクエリを実行
  • Overview
  • 手順

このチュートリアルでは、Atlas クラスターのsample_mflix.embedded_moviesコレクション内の plot_embeddingフィールド内のベクトルに対して ANN検索を実行する方法について説明します。 これを示すには、次の手順に従います。

  1. sample_mflix.embedded_moviesコレクション内のplot_embeddingという名前の数値フィールドに Atlas Vector Search インデックスを作成します。

  2. sample_mflix.embedded_movies コレクションのplot_embedding フィールドに対して Atlas Vector Search クエリを実行します。

Atlas の サンプル データ セット からの映画データを含むコレクションを使用します。

  • MongoDB バージョン 6.0.11 または v7.0.2 以降( RCを含む)を搭載した Atlas クラスター。

  • Atlas クラスターにロードされたサンプル データ

    注意

    Atlas でベクトルの効率的な保存と取り込みを行うために、サンプルデータの埋め込みを BSON ベクトルに変換することもできます。詳細については、「ネイティブ埋め込みを BSON ベクトルに変換する方法」を参照してください。

  • Atlas クラスターでクエリを実行するには、次のいずれかのクライアントが必要です。

    • mongosh

    • MongoDB Java ドライバー

    • MongoDB Go ドライバー

    • MongoDB ノード ドライバー

    • PyMongo

    注意

    集計ステージでは任意のMongoDBドライバーを使用して クエリを実行できます。には、前述のリストにあるドライバーのみの例が含まれています。

    Atlas Vector Search は、Atlas CLI を使用して作成したローカル Atlas 配置で使用することもできます。 詳細については、「 Atlas 配置のローカル配置の作成 」を参照してください。


➤ [言語を選択 ] ドロップダウン メニューを使用して、Atlas Vector Search インデックスを作成し、クエリを実行するために使用するクライアントを選択します。


このセクションでは、フィールドに対してベクトル クエリを実行するために、 sample_mflix.embedded_moviesコレクションのplot_embeddingフィールドに Atlas Vector Search インデックスを作成する方法を説明します。

Atlas Vector Search インデックスを作成するには、プロジェクトに対するProject Data Access Admin以上のアクセス権が必要です。

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

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

  3. まだ表示されていない場合は、サイドバーの Clusters をクリックしてください。

    [ Clusters (クラスター) ] ページが表示されます。

2

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

  1. サイドバーで、 Services見出しの下のAtlas Searchをクリックします。

  2. [ Select data sourceドロップダウンからクラスターを選択し、[ Go to Atlas Search ] をクリックします。

    Atlas Searchページが表示されます。

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

  2. データベースを展開し、コレクションを選択します。

  3. コレクションのSearch Indexesタブをクリックします。

    Atlas Searchページが表示されます。

  1. クラスタの名前をクリックします。

  2. [Atlas Search] タブをクリックします。

    Atlas Searchページが表示されます。

3
  1. [Create Search Index] をクリックします。

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

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

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

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

4
  1. デフォルトの定義を、以下のインデックス定義で置き換えます。

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

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

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

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

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

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

6

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

1

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

2

use sample_mflix
switched to db sample_mflix
3

次のインデックス定義は、Atlas Vector Search インデックスで、 plot_embeddingフィールドを タイプとして、vector フィールドとgenres yearフィールドを タイプとしてインデックス化します。filterplot_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、 euclidean距離を使用して類似性を測定します。

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 "type": "filter",
14 "path": "genres"
15 },
16 {
17 "type": "filter",
18 "path": "year"
19 }
20 ]
21 }
22);
1

Go ドライバーを依存関係としてプロジェクトに追加します。

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

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

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

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

3
go run vector-index.go
2024/10/17 09:38:21 Creating the index.
2024/10/17 09:38:22 Polling to confirm successful index creation.
2024/10/17 09:38:22 NOTE: This may take up to a minute.
2024/10/17 09:38:48 Name of Index Created: vector_index
1
  1. 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。

2

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

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

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

3

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

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

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

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 "type": "filter",
27 "path": "genres"
28 },
29 {
30 "type": "filter",
31 "path": "year"
32 }
33 ]
34 }
35 }
36
37 // run the helper method
38 const result = await collection.createSearchIndex(index);
39 console.log(`New search index named ${result} is building.`);
40
41 // wait for the index to be ready to query
42 console.log("Polling to check if the index is ready. This may take up to a minute.")
43 let isQueryable = false;
44 while (!isQueryable) {
45 const cursor = collection.listSearchIndexes();
46 for await (const index of cursor) {
47 if (index.name === result) {
48 if (index.queryable) {
49 console.log(`${result} is ready for querying.`);
50 isQueryable = true;
51 } else {
52 await new Promise(resolve => setTimeout(resolve, 5000));
53 }
54 }
55 }
56 }
57 } finally {
58 await client.close();
59 }
60}
61run().catch(console.dir);

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

3
node vector-index.js
vector_index
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
2

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

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 "type": "filter",
25 "path": "genres"
26 },
27 {
28 "type": "filter",
29 "path": "year"
30 }
31 ]
32 },
33 name="vector_index",
34 type="vectorSearch",
35)
36
37result = collection.create_search_index(model=search_index_model)
38print("New search index named " + result + " is building.")
39# Wait for initial sync to complete
40print("Polling to check if the index is ready. This may take up to a minute.")
41predicate=None
42if predicate is None:
43 predicate = lambda index: index.get("queryable") is True
44
45while True:
46 indices = list(collection.list_search_indexes(result))
47 if len(indices) and predicate(indices[0]):
48 break
49 time.sleep(5)
50print(result + " is ready for querying.")
51
52client.close()

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

3
python vector-index.py
vector_index

このセクションでは、 $vectorSearchステージを使用して、 sample_mflix.embedded_moviesコレクション内のインデックス付きベクトル データをクエリする方法を説明します。 これらのサンプル クエリでは、セマンティック検索を実行するデータを事前にフィルタリングするためにクエリで使用できるさまざまなクエリおよび集計パイプライン演算子も示されています。

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

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

  3. まだ表示されていない場合は、サイドバーの Clusters をクリックしてください。

    [ Clusters (クラスター) ] ページが表示されます。

2

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

Data Explorerが表示されます。

3
  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。

4

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

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

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

  1. [</> Text] をクリックします。

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

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

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

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

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

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

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

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

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

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

    1[
    2 {
    3 "$vectorSearch": {
    4 "index": "vector_index",
    5 "path": "plot_embedding",
    6 "filter": {
    7 "$or": [{
    8 "genres": { "$ne": "Crime" }
    9 }, {
    10 "$and": [{
    11 "year": { "$lte": 2015 }
    12 }, {
    13 "genres": { "$eq": "Action" }
    14 }]
    15 }]
    16 },
    17 "queryVector": 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    26plot: "A martial arts instructor from the police force gets imprisoned after …"
    27genres: Array (2)
    28title: "Kung Fu Killer"
    29year: 2014
    30
    31plot: "A young woman is trained by a martial arts specialist to become a prof…"
    32genres: Array (3)
    33title: "Naked Killer"
    34year: 1992
    35score: 0.7693743109703064
    36
    37plot: "The life of the greatest karate master of a generation."
    38genres: Array (3)
    39title: "The Real Miyagi"
    40year: 2015
    41score: 0.768799901008606
    42
    43plot: "At his new high school, a rebellious teen is lured into an underground…"
    44genres: Array (3)
    45title: "Never Back Down"
    46year: 2008
    47score: 0.768179714679718
    48
    49score: 0.7679706811904907
    50plot: "Three young martial arts masters emerge from the back streets of Hong …"
    51genres: Array (2)
    52title: "Dragon Tiger Gate"
    53year: 2006
    54
    55title: "Shaolin Soccer"
    56score: 0.7660527229309082
    57year: 2001
    58plot: "A young Shaolin follower reunites with his discouraged brothers to for…"
    59genres: Array (3)

注意

Pipeline Outputペインにはクエリの結果が表示されます。

5

Data Explorerでは返されるドキュメント内のすべての値が表示されない場合があります。 クエリ パスで検索したフィールドのすべての値を表示するには、ドキュメント内のフィールドを展開します。

1

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

2
use sample_mflix
switched to db sample_mflix
3

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

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

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

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

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

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

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

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding",
6 "filter": {
7 "$and": [{
8 "genres": {
9 "$nin": ["Drama", "Western", "Crime"] ,
10 "$in": ["Action", "Adventure", "Family"]
11 },
12 }, {
13 "year": {
14 "$gte": 1960,
15 "$lte": 2000
16 }
17 }]
18 },
19 "queryVector": 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2 {
3 year: 1993,
4 plot: 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.',
5 genres: [ 'Action', 'Adventure', 'Thriller' ],
6 title: 'Cliffhanger',
7 score: 0.7694521546363831
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9 {
10 plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
11 genres: [ 'Action', 'Adventure', 'Comedy' ],
12 title: "It's a Mad, Mad, Mad, Mad World",
13 year: 1963,
14 score: 0.7638954520225525
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16 {
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18 plot: 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.',
19 genres: [ 'Action', 'Adventure', 'Comedy' ],
20 title: 'Hudson Hawk',
21 score: 0.7538407444953918
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23 {
24 plot: 'In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.',
25 genres: [ 'Action', 'Sci-Fi' ],
26 title: 'Escape from New York',
27 year: 1981,
28 score: 0.7487208843231201
29 },
30 {
31 plot: 'Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.',
32 genres: [ 'Action', 'Thriller' ],
33 title: 'The Romanov Stones',
34 year: 1993,
35 score: 0.7467736005783081
36 },
37 {
38 plot: 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.',
39 genres: [ 'Action', 'Comedy', 'Adventure' ],
40 title: 'Crime Busters',
41 year: 1977,
42 score: 0.7437351942062378
43 },
44 {
45 plot: 'In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.',
46 genres: [ 'Action', 'Adventure', 'Comedy' ],
47 title: 'Three Kings',
48 year: 1999,
49 score: 0.7425670623779297
50 },
51 {
52 plot: 'A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...',
53 genres: [ 'Action', 'Sci-Fi', 'Thriller' ],
54 title: 'Black Moon Rising',
55 year: 1986,
56 score: 0.7397696375846863
57 },
58 {
59 plot: 'A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.',
60 genres: [ 'Action', 'Adventure', 'Horror' ],
61 title: 'Deep Rising',
62 year: 1998,
63 score: 0.7392246127128601
64 },
65 {
66 plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
67 genres: [ 'Action', 'Adventure', 'Comedy' ],
68 title: 'That Man from Rio',
69 year: 1964,
70 score: 0.7357995510101318
71 }
72]

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

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

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

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

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

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

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

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

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

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

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

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

1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/bson"
9 "go.mongodb.org/mongo-driver/mongo"
10 "go.mongodb.org/mongo-driver/mongo/options"
11)
12
13func main() {
14 ctx := context.Background()
15
16 // Replace the placeholder with your Atlas connection string
17 const uri = "<connection-string>"
18
19 // Connect to your Atlas cluster
20 clientOptions := options.Client().ApplyURI(uri)
21 client, err := mongo.Connect(ctx, clientOptions)
22 if err != nil {
23 log.Fatalf("failed to connect to the server: %v", err)
24 }
25 defer func() { _ = client.Disconnect(ctx) }()
26
27 // Set the namespace
28 coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30 queryVector := [1536]float64{
31 -0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 0.010111088, 0.0074623227, 0.009555174, 0.008338705, -0.002240005, -0.0010603234, -0.004973792, 0.003391073, 0.021543289, 0.013341927, 0.0005980159, 0.010693162, 0.005336771, 0.016062634, 0.005768421, 0.005186347, 0.039790336, 0.0021942237, -0.0026275094, 0.010431555, 0.0042151334, -0.0050359233, 0.025768232, -0.021451725, 0.01833861, -0.01836477, -0.013433489, 0.030006256, -0.014793842, 0.017475309, 0.0020585153, -0.012975678, -0.017266022, -0.01593183, -0.014257549, 0.0010676811, -0.007887433, -0.0045911926, 0.00012303676, -0.0014976967, 0.03552615, 0.0065630507, -0.037435878, 0.011929252, -0.00939167, 0.016768971, 0.01223664, 0.007789331, -0.037200432, 0.013145722, 0.00896002, 0.021857215, 0.010333453, 0.021582529, -0.007089534, -0.007154935, -0.02485261, 0.0040254686, -0.00088864425, 0.023466095, -0.020719228, -0.006690584, -0.021006994, -0.018286288, 0.025545865, -0.0096598165, 0.008803056, -0.023021365, -0.040078104, 0.015408617, 0.017043658, -0.011242535, 0.0063537657, -0.026618453, 0.0071614753, 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0.018325528, -0.03152357, -0.008809595, 0.014649959, -0.008345244, 0.0066415328, -0.005523165, 0.0043492066, -0.0015892589, 0.0048855, 0.034453563, -0.03837766, 0.0068410076, -0.0042151334, -0.0067429054, 0.0055689462, -0.011733048, -0.0212032, 0.016847452, -0.0022220195, 0.0059351954, -0.00449963, 0.02251123, -0.01020265, 0.023361452, -0.0032455544, 0.016180357, 0.0049443613, -0.0064747585, -0.03259616, 0.012321662, 0.020104453, 0.009954124, -0.019411195, 0.0048102876, -0.000392614, 0.012184318, 0.0044276887, 0.005634348, -0.020562263, 0.015722545, -0.005179807, -0.0067952266, 0.0027861083, 0.0024198592, -0.0020585153, 0.0018525004, -0.045100946, -0.010176489, -0.012956058, 0.0013497255, 0.0105361985, 0.003796563, -0.0106016, -0.013126101, 0.0050359233, 0.015003128, -0.0075800456, -0.015722545, -0.01755379, -0.00978408, -0.02940456, 0.017606111, 0.016612006, -0.016912855, 0.025441224, 0.0054741143, 0.00448001, 0.009470152, 0.015382457, -0.008332164, -0.019123428, 0.024564842, 0.016860534, 0.008286383, -0.007141855, 0.006559781, 0.016625088, -0.01840401, -0.011602244, -0.00489858, -0.0073184394, -0.008809595, -0.0018459603, -0.01629808, -0.005542786, 0.0064257076, 0.010379234, 0.014663039, 0.034872133, -0.013355007, 0.027285548, 0.011654565, -0.004032009, 0.02323065, -0.02653997, -0.0009941043, 0.002946342, 0.010667001, 0.008345244, 0.018626377, 0.04821406, 0.031392768, 0.010281132, 0.026069079, 0.002735422, 0.01182461, -0.01593183, 0.006585941, -0.010071847, 0.024564842, -0.0025261368, 0.004293615, -0.0068606283, -0.0066448026, -0.0074100015, -0.0014347476, 0.021530207, -0.010418476, 0.018495573, -0.0034924455, -0.014165987, -0.004784127, -0.012472086, 0.004417878, -0.0030313642, -0.010084927, -0.010954768, 0.01508161, 0.0010047321, 0.0042347535, -0.03345946, -0.00027346043, 0.014793842, -0.019882087, 0.012772933, 0.021490967, 0.0031932332, 0.0093589695, 0.00090172456, 0.0048102876, 0.0070045115, -0.0045584915, 0.015840268, 0.024342475, -0.0091300635, 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-0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783,
32 }
33
34 genresFilterCondition := bson.D{
35 {"$and", bson.A{
36 bson.D{{"genres", bson.D{{"$nin", bson.A{"Drama", "Western", "Crime"}}}}},
37 bson.D{{"genres", bson.D{{"$in", bson.A{"Action", "Adventure", "Family"}}}}},
38 }}}
39
40 yearFilterCondition := bson.D{
41 {"$and", bson.A{
42 bson.D{{"year", bson.D{{"$gte", 1960}}}},
43 bson.D{{"year", bson.D{{"$lte", 2000}}}},
44 }},
45 }
46
47 filter := bson.D{{"$and", bson.A{
48 genresFilterCondition, yearFilterCondition,
49 }},
50 }
51
52 vectorSearchStage := bson.D{
53 {"$vectorSearch", bson.D{
54 {"index", "vector_index"},
55 {"path", "plot_embedding"},
56 {"filter", filter},
57 {"queryVector", queryVector},
58 {"numCandidates", 200},
59 {"limit", 10},
60 }},
61 }
62
63 projectStage := bson.D{
64 {"$project", bson.D{
65 {"_id", 0},
66 {"plot", 1},
67 {"title", 1},
68 {"genres", 1},
69 {"year", 1},
70 {"score", bson.D{{"$meta", "vectorSearchScore"}}},
71 }}}
72
73 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
74 if err != nil {
75 log.Fatalf("failed to retrieve data from the server: %v", err)
76 }
77 // display the results
78 type ProjectedMovieResult struct {
79 Title string `bson:"title"`
80 Plot string `bson:"plot"`
81 Genres []string `bson:"genres"`
82 Year int32 `bson:"year"`
83 Score float64 `bson:"score"`
84 }
85
86 var results []ProjectedMovieResult
87 if err = cursor.All(ctx, &results); err != nil {
88 log.Fatalf("failed to unmarshal results to ProjectedMovieResult objects: %v", err)
89 }
90 for _, result := range results {
91 fmt.Printf("Title: %v \nPlot: %v \nGenres: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Genres, result.Year, result.Score)
92 }
93}

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

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

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

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

1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/bson"
9 "go.mongodb.org/mongo-driver/mongo"
10 "go.mongodb.org/mongo-driver/mongo/options"
11)
12
13func main() {
14 ctx := context.Background()
15
16 // Replace the placeholder with your Atlas connection string
17 const uri = "<connection-string>"
18
19 // Connect to your Atlas cluster
20 clientOptions := options.Client().ApplyURI(uri)
21 client, err := mongo.Connect(ctx, clientOptions)
22 if err != nil {
23 log.Fatalf("failed to connect to the server: %v", err)
24 }
25 defer func() { _ = client.Disconnect(ctx) }()
26
27 // Set the namespace
28 coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30 queryVector := [1536]float64{
31 -0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 0.016192842, 0.0026484078, 0.002417546, -0.007764761, -0.0053781047, 0.0016843157, 0.009071894, 0.0026158919, -0.016465975, 0.019054228, 0.030720878, 0.028145632, -0.0124340225, -0.011491066, -0.01273967, -0.0076542073, 0.00685432, 0.002936172, 0.03194347, -0.030798918, -0.004961903, 0.012661632, -0.024646938, 0.0085256295, -0.01133499, 0.016648063, -0.017857648, 0.01627088, -0.020823086, -0.002233832, 0.008330535, 0.024894057, 0.008558145, 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0.041802246, -0.01003436, 0.0097937435, 0.003641764, 0.022787036, -0.0017867404, -0.0026110145, 0.028145632, 0.011152902, 0.004311588, -0.021915615, -0.009182448, 0.02077106, -0.006171489, -0.0055309287, -0.019379387, 0.0022647218, -0.021213274, -0.014983257, 0.01158211, -0.0149572445, -0.021278305, -0.011725179, -0.021798559, 0.019691538, -0.04835742, 0.011536588, 0.029082086, -0.0006828307, -0.020003688, -0.0067957914, -0.16679278, 0.015646579, 0.037119977, -0.026480826, 0.0109317945, 0.0053488407, 0.013825696, -0.00035117008, -0.0013892354, -0.0014022416, 0.021915615, 0.008974346, -0.03274986, -0.0078688115, 0.023034155, 0.003518204, -0.017857648, 0.025037127, 0.0137606645, 0.02411368, 0.025765479, 0.0024338039, -0.00607069, -0.005114727, -0.009071894, -0.0094295675, 0.021096218, 0.016400944, -0.013175381, -0.0047440478, -0.015789647, -0.006548672, 0.017766604, 0.0077452515, 0.0044221417, 0.00838256, 0.0007568041, -0.015386452, -0.017649548, 0.014306929, -0.003066235, 0.02783348, -0.006984383, -0.02586953, 0.005108224, 0.02432178, 0.00885729, 0.0023297535, 0.007166471, -0.0021281557, -0.0052740546, -0.0035149525, 0.014085822, 0.00063893443, 0.031215118, -0.00425306, 0.014202879, -0.018351888, -0.0032158075, -0.0055081677, 0.003999437, -0.0017753599, 0.022748018, -0.023684472, -0.007946849, -0.019145273, -0.022487892, 0.018247837, -0.004535947, 0.008993856, -0.0043018335, 0.005498413, -0.0022647218, -0.023112195, 0.0055114194, 0.008818271, -0.00536835, 0.022604948, -0.020406883, -0.024386812, -0.014931232, 0.01761053, 0.0037588205, -0.0034986946, 0.01831287, 0.036001436, -0.000008446474, -0.024907064, -0.0036612733, -0.011725179, 0.023047162, -0.029758412, -0.014554049, -0.012934765, 0.015412465, 0.021850582, -0.011523581, -0.0022858572, -0.0040742233, -0.0056187212, 0.010307493, -0.019184291, 0.005192765, 0.008018384, 0.022722006, -0.0058138156, 0.03771827, 0.01028148, 0.029212149, -0.0054366332, 0.0014079319, -0.0009974206, 0.011627631, 0.026480826, 0.0025346025, 0.018937172, 0.0085971635, -0.012395004, 0.011608122, 0.00065397297, 0.040085416, 0.008883302, -0.0013339586, -0.0064413697, -0.007296534, 0.0028467537, -0.092917, -0.00801188, 0.016192842, 0.027469303, -0.033166062, 0.017662555, -0.002692304, 0.036079474, -0.0144760115, 0.020445902, -0.00036641184, -0.018442933, -0.008493113, -0.01476215, 0.018703058, 0.001552627, 0.0065876907, -0.017974706, -0.0076542073, 0.022149729, -0.010066876, 0.004932639, -0.0061292187, 0.0016436711, -0.007959855, 0.0031393955, -0.0324117, 0.037093967, 0.007114446, 0.019821601, -0.016518, -0.0034596757, 0.021668496, -0.03332214, 0.012785193, -0.007810283, -0.018833121, -0.0029963262, 0.0210572, -0.019392392, 0.0021135237, 0.015685597, 0.013116853, -0.027495317, 0.011250449, -0.0071924836, -0.019106254, 0.0107562095, 0.014007784, -0.034492705, -0.03394644, -0.024750987, -0.041047882, -0.006685238, 0.0068868357, 0.008694711, 0.019093247, 0.01261611, -0.009748221, -0.0027394518, 0.014918226, -0.0032710843, -0.0044936766, 0.01546449, 0.018208819, 0.004288827, -0.021538433, -0.026897028, 0.023879565, -0.018299863, -0.012245431, 0.015516515, -0.040891804, -0.011816223, -0.02221476, 0.012082852, -0.02417871, -0.009338523, 0.010047366, -0.027079115, -0.016010754, -0.021863589, -0.004305085, -0.027261203, 0.024100672, 0.000711282, 0.001071394, -0.0037263047, -0.007829792, -0.0049456456, -0.021603463, 0.03274986, 0.01866404, -0.032567773, -0.0044318964, 0.017935688, 0.018638028, -0.015282402, -0.009377542, 0.03012259, 0.015178352, -0.004792821, -0.048305396, 0.007075427, 0.020784067, 0.010678172, 0.0019509449, 0.0045684627, 0.02242286, -0.011374009, 0.024477856, -0.0021736778, -0.021291312, -0.009188951, 0.02692304, -0.0038628709, -0.02442583, -0.029394237, 0.019054228, 0.01606278, -0.0035051976, -0.00012284856, 0.01911926, 0.0088768, 0.007036408, 0.0015461239, -0.0030711126, 0.0034629272, -0.015152339, 0.02097916, -0.031163093, 0.0061909985, 0.012694148, -0.021902608, -0.030044552, 0.031553283, 0.0002003783, -0.037119977, 0.0003367412, 0.015347433, 0.003105254, 0.034102518, 0.001911926, -0.008102925, 0.0003306445, 0.011647142, -0.004288827, 0.019665524, -0.011907267, 0.01391674, 0.0014428863, -0.01655702, -0.0020387375, 0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914,
32 }
33
34 genresFilterCondition := bson.D{{"genres", bson.D{{"$ne", "Crime"}}}}
35
36 yearAndGenresFilterCondition := bson.D{
37 {"$and", bson.A{
38 bson.D{{"year", bson.D{{"$lte", 2015}}}},
39 bson.D{{"genres", bson.D{{"$eq", "Action"}}}},
40 }},
41 }
42
43 filter := bson.D{{"$or", bson.A{
44 genresFilterCondition, yearAndGenresFilterCondition,
45 }},
46 }
47
48 vectorSearchStage := bson.D{
49 {"$vectorSearch", bson.D{
50 {"index", "vector_index"},
51 {"path", "plot_embedding"},
52 {"filter", filter},
53 {"queryVector", queryVector},
54 {"numCandidates", 200},
55 {"limit", 10},
56 }},
57 }
58
59 projectStage := bson.D{
60 {"$project", bson.D{
61 {"_id", 0},
62 {"plot", 1},
63 {"title", 1},
64 {"genres", 1},
65 {"year", 1},
66 {"score", bson.D{{"$meta", "vectorSearchScore"}}},
67 }}}
68
69 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
70 if err != nil {
71 log.Fatalf("failed to retrieve data from the server: %v", err)
72 }
73 // display the results
74 type ProjectedMovieResult struct {
75 Title string `bson:"title"`
76 Plot string `bson:"plot"`
77 Genres []string `bson:"genres"`
78 Year int32 `bson:"year"`
79 Score float64 `bson:"score"`
80 }
81
82 var results []ProjectedMovieResult
83 if err = cursor.All(ctx, &results); err != nil {
84 log.Fatalf("failed to unmarshal results to ProjectedMovieResult objects: %v", err)
85 }
86 for _, result := range results {
87 fmt.Printf("Title: %v \nPlot: %v \nGenres: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Genres, result.Year, result.Score)
88 }
89}
3

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。 詳しくは、「ドライバーによる接続 」を参照してください。

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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。 詳しくは、「ドライバーによる接続 」を参照してください。

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

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

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

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

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

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

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

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

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

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

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

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

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

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async function run() {
9 try {
10 await client.connect();
11
12 // set namespace
13 const database = client.db("sample_mflix");
14 const coll = database.collection("embedded_movies");
15
16 // define pipeline
17 const agg = [
18 {
19 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding',
22 'filter': {
23 '$or': [
24 {
25 'genres': {
26 '$ne': 'Crime'
27 }
28 }, {
29 '$and': [
30 {
31 'year': {
32 '$lte': 2015
33 }
34 }, {
35 'genres': {
36 '$eq': 'Action'
37 }
38 }
39 ]
40 }
41 ]
42 },
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0.041802246, -0.01003436, 0.0097937435, 0.003641764, 0.022787036, -0.0017867404, -0.0026110145, 0.028145632, 0.011152902, 0.004311588, -0.021915615, -0.009182448, 0.02077106, -0.006171489, -0.0055309287, -0.019379387, 0.0022647218, -0.021213274, -0.014983257, 0.01158211, -0.0149572445, -0.021278305, -0.011725179, -0.021798559, 0.019691538, -0.04835742, 0.011536588, 0.029082086, -0.0006828307, -0.020003688, -0.0067957914, -0.16679278, 0.015646579, 0.037119977, -0.026480826, 0.0109317945, 0.0053488407, 0.013825696, -0.00035117008, -0.0013892354, -0.0014022416, 0.021915615, 0.008974346, -0.03274986, -0.0078688115, 0.023034155, 0.003518204, -0.017857648, 0.025037127, 0.0137606645, 0.02411368, 0.025765479, 0.0024338039, -0.00607069, -0.005114727, -0.009071894, -0.0094295675, 0.021096218, 0.016400944, -0.013175381, -0.0047440478, -0.015789647, -0.006548672, 0.017766604, 0.0077452515, 0.0044221417, 0.00838256, 0.0007568041, -0.015386452, -0.017649548, 0.014306929, -0.003066235, 0.02783348, -0.006984383, -0.02586953, 0.005108224, 0.02432178, 0.00885729, 0.0023297535, 0.007166471, -0.0021281557, -0.0052740546, -0.0035149525, 0.014085822, 0.00063893443, 0.031215118, -0.00425306, 0.014202879, -0.018351888, -0.0032158075, -0.0055081677, 0.003999437, -0.0017753599, 0.022748018, -0.023684472, -0.007946849, -0.019145273, -0.022487892, 0.018247837, -0.004535947, 0.008993856, -0.0043018335, 0.005498413, -0.0022647218, -0.023112195, 0.0055114194, 0.008818271, -0.00536835, 0.022604948, -0.020406883, -0.024386812, -0.014931232, 0.01761053, 0.0037588205, -0.0034986946, 0.01831287, 0.036001436, -0.000008446474, -0.024907064, -0.0036612733, -0.011725179, 0.023047162, -0.029758412, -0.014554049, -0.012934765, 0.015412465, 0.021850582, -0.011523581, -0.0022858572, -0.0040742233, -0.0056187212, 0.010307493, -0.019184291, 0.005192765, 0.008018384, 0.022722006, -0.0058138156, 0.03771827, 0.01028148, 0.029212149, -0.0054366332, 0.0014079319, -0.0009974206, 0.011627631, 0.026480826, 0.0025346025, 0.018937172, 0.0085971635, -0.012395004, 0.011608122, 0.00065397297, 0.040085416, 0.008883302, -0.0013339586, -0.0064413697, -0.007296534, 0.0028467537, -0.092917, -0.00801188, 0.016192842, 0.027469303, -0.033166062, 0.017662555, -0.002692304, 0.036079474, -0.0144760115, 0.020445902, -0.00036641184, -0.018442933, -0.008493113, -0.01476215, 0.018703058, 0.001552627, 0.0065876907, -0.017974706, -0.0076542073, 0.022149729, -0.010066876, 0.004932639, -0.0061292187, 0.0016436711, -0.007959855, 0.0031393955, -0.0324117, 0.037093967, 0.007114446, 0.019821601, -0.016518, -0.0034596757, 0.021668496, -0.03332214, 0.012785193, -0.007810283, -0.018833121, -0.0029963262, 0.0210572, -0.019392392, 0.0021135237, 0.015685597, 0.013116853, -0.027495317, 0.011250449, -0.0071924836, -0.019106254, 0.0107562095, 0.014007784, -0.034492705, -0.03394644, -0.024750987, -0.041047882, -0.006685238, 0.0068868357, 0.008694711, 0.019093247, 0.01261611, -0.009748221, -0.0027394518, 0.014918226, -0.0032710843, -0.0044936766, 0.01546449, 0.018208819, 0.004288827, -0.021538433, -0.026897028, 0.023879565, -0.018299863, -0.012245431, 0.015516515, -0.040891804, -0.011816223, -0.02221476, 0.012082852, -0.02417871, -0.009338523, 0.010047366, -0.027079115, -0.016010754, -0.021863589, -0.004305085, -0.027261203, 0.024100672, 0.000711282, 0.001071394, -0.0037263047, -0.007829792, -0.0049456456, -0.021603463, 0.03274986, 0.01866404, -0.032567773, -0.0044318964, 0.017935688, 0.018638028, -0.015282402, -0.009377542, 0.03012259, 0.015178352, -0.004792821, -0.048305396, 0.007075427, 0.020784067, 0.010678172, 0.0019509449, 0.0045684627, 0.02242286, -0.011374009, 0.024477856, -0.0021736778, -0.021291312, -0.009188951, 0.02692304, -0.0038628709, -0.02442583, -0.029394237, 0.019054228, 0.01606278, -0.0035051976, -0.00012284856, 0.01911926, 0.0088768, 0.007036408, 0.0015461239, -0.0030711126, 0.0034629272, -0.015152339, 0.02097916, -0.031163093, 0.0061909985, 0.012694148, -0.021902608, -0.030044552, 0.031553283, 0.0002003783, -0.037119977, 0.0003367412, 0.015347433, 0.003105254, 0.034102518, 0.001911926, -0.008102925, 0.0003306445, 0.011647142, -0.004288827, 0.019665524, -0.011907267, 0.01391674, 0.0014428863, -0.01655702, -0.0020387375, 0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914],
44 'numCandidates': 200,
45 'limit': 10
46 }
47 }, {
48 '$project': {
49 '_id': 0,
50 'title': 1,
51 'genres': 1,
52 'plot': 1,
53 'released': 1,
54 'score': {
55 '$meta': 'vectorSearchScore'
56 }
57 }
58 }
59 ];
60
61 // run pipeline
62 const result = coll.aggregate(agg);
63
64 // print results
65 await result.forEach((doc) => console.dir(JSON.stringify(doc)));
66 } finally {
67 await client.close();
68 }
69}
70run().catch(console.dir);
3

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。 詳しくは、「ドライバーによる接続 」を参照してください。

4

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

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

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

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

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

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

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

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

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

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

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

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

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

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8 {
9 '$vectorSearch': {
10 'index': 'vector_index',
11 'path': 'plot_embedding',
12 'filter': {
13 '$or': [
14 {
15 'genres': {
16 '$ne': 'Crime'
17 }
18 }, {
19 '$and': [
20 {
21 'year': {
22 '$lte': 2015
23 }
24 }, {
25 'genres': {
26 '$eq': 'Action'
27 }
28 }
29 ]
30 }
31 ]
32 },
33 'queryVector': [-0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 0.016192842, 0.0026484078, 0.002417546, -0.007764761, -0.0053781047, 0.0016843157, 0.009071894, 0.0026158919, -0.016465975, 0.019054228, 0.030720878, 0.028145632, -0.0124340225, -0.011491066, -0.01273967, -0.0076542073, 0.00685432, 0.002936172, 0.03194347, -0.030798918, -0.004961903, 0.012661632, -0.024646938, 0.0085256295, -0.01133499, 0.016648063, -0.017857648, 0.01627088, -0.020823086, -0.002233832, 0.008330535, 0.024894057, 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-0.0032710843, -0.0044936766, 0.01546449, 0.018208819, 0.004288827, -0.021538433, -0.026897028, 0.023879565, -0.018299863, -0.012245431, 0.015516515, -0.040891804, -0.011816223, -0.02221476, 0.012082852, -0.02417871, -0.009338523, 0.010047366, -0.027079115, -0.016010754, -0.021863589, -0.004305085, -0.027261203, 0.024100672, 0.000711282, 0.001071394, -0.0037263047, -0.007829792, -0.0049456456, -0.021603463, 0.03274986, 0.01866404, -0.032567773, -0.0044318964, 0.017935688, 0.018638028, -0.015282402, -0.009377542, 0.03012259, 0.015178352, -0.004792821, -0.048305396, 0.007075427, 0.020784067, 0.010678172, 0.0019509449, 0.0045684627, 0.02242286, -0.011374009, 0.024477856, -0.0021736778, -0.021291312, -0.009188951, 0.02692304, -0.0038628709, -0.02442583, -0.029394237, 0.019054228, 0.01606278, -0.0035051976, -0.00012284856, 0.01911926, 0.0088768, 0.007036408, 0.0015461239, -0.0030711126, 0.0034629272, -0.015152339, 0.02097916, -0.031163093, 0.0061909985, 0.012694148, -0.021902608, 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0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914],
34 'numCandidates': 200,
35 'limit': 10
36 }
37 }, {
38 '$project': {
39 '_id': 0,
40 'title': 1,
41 'genres': 1,
42 'plot': 1,
43 'year': 1,
44 'score': {
45 '$meta': 'vectorSearchScore'
46 }
47 }
48 }
49]
50
51# run pipeline
52result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
53
54# print results
55for i in result:
56 print(i)
3

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。 詳しくは、「ドライバーによる接続 」を参照してください。

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

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