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
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。
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。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。
前提条件
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。
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。
Atlas Vector Search インデックスの作成
➤ [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。
必要なアクセス権
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。
手順
Atlas Atlasで、プロジェクトの {0 ページにGoします。GoClusters
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
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。
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。
GoAtlas Searchクラスターの ページに します。
GoAtlas Searchページには、サイドバー、Data Explorer 、またはクラスターの詳細ページから できます。
サイドバーで、 Services見出しの下のAtlas Searchをクリックします。
[ 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。
クラスターの [Browse Collections] ボタンをクリックします。
データベースを展開し、コレクションを選択します。
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。
クラスタの名前をクリックします。
[Atlas Search] タブをクリックします。
Atlas Searchページが表示されます。
Atlas Vector Search インデックスを定義します。
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 }
mongosh
でクラスターに接続します。
ターミナル ウィンドウでmongosh
を開き、クラスターに接続します。 接続の詳細な手順については、「 mongosh 経由での接続 」を参照してください。
db.collection.createSearchIndex()
メソッドを実行します。
次のインデックス定義は、Atlas 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。filter
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
距離を使用して類似性を測定します。
1 db.embedded_movies.createSearchIndex( 2 "vector_index", 3 "vectorSearch", 4 { 5 "fields": [ 6 { 7 "type": "vector", 8 "path": "plot_embedding", 9 "numDimensions": 1536, 10 "similarity": "euclidean" 11 }, 12 { 13 "type": "filter", 14 "path": "genres" 15 }, 16 { 17 "type": "filter", 18 "path": "year" 19 } 20 ] 21 } 22 );
インデックスを定義します。
vector-index.go
という名前のファイルを作成します。次のコードをコピーして、ファイルに貼り付け、<connectionString>
プレースホルダー値を置き換えます。
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 "time" 8 9 "go.mongodb.org/mongo-driver/bson" 10 "go.mongodb.org/mongo-driver/mongo" 11 "go.mongodb.org/mongo-driver/mongo/options" 12 ) 13 14 func main() { 15 ctx := context.Background() 16 17 // Replace the placeholder with your Atlas connection string 18 const uri = "<connectionString>" 19 20 // Connect to your Atlas cluster 21 clientOptions := options.Client().ApplyURI(uri) 22 client, err := mongo.Connect(ctx, clientOptions) 23 if err != nil { 24 log.Fatalf("failed to connect to the server: %v", err) 25 } 26 defer func() { _ = client.Disconnect(ctx) }() 27 28 // Set the namespace 29 coll := client.Database("sample_mflix").Collection("embedded_movies") 30 indexName := "vector_index" 31 opts := options.SearchIndexes().SetName(indexName).SetType("vectorSearch") 32 33 type vectorDefinitionField struct { 34 Type string `bson:"type"` 35 Path string `bson:"path"` 36 NumDimensions int `bson:"numDimensions"` 37 Similarity string `bson:"similarity"` 38 } 39 40 type filterField struct { 41 Type string `bson:"type"` 42 Path string `bson:"path"` 43 } 44 45 type indexDefinition struct { 46 Fields []vectorDefinitionField `bson:"fields"` 47 } 48 49 vectorDefinition := vectorDefinitionField{ 50 Type: "vector", 51 Path: "plot_embedding", 52 NumDimensions: 1536, 53 Similarity: "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。
MongoDB Java Sync Driver をプロジェクトに追加します。
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,)' }
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。
インデックスを定義します。
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。
1 import com.mongodb.client.ListSearchIndexesIterable; 2 import com.mongodb.client.MongoClient; 3 import com.mongodb.client.MongoClients; 4 import com.mongodb.client.MongoCollection; 5 import com.mongodb.client.MongoCursor; 6 import com.mongodb.client.MongoDatabase; 7 import com.mongodb.client.model.SearchIndexModel; 8 import com.mongodb.client.model.SearchIndexType; 9 import org.bson.Document; 10 import org.bson.conversions.Bson; 11 12 import java.util.Arrays; 13 import java.util.Collections; 14 import java.util.List; 15 16 public class VectorIndex { 17 18 public static void main(String[] args) { 19 20 // Replace the placeholder with your Atlas connection string 21 String uri = "<connectionString>"; 22 23 // Connect to your Atlas cluster 24 try (MongoClient mongoClient = MongoClients.create(uri)) { 25 26 // Set the namespace 27 MongoDatabase database = mongoClient.getDatabase("sample_mflix"); 28 MongoCollection<Document> collection = database.getCollection("embedded_movies"); 29 30 // Define the index details with the filter fields 31 String indexName = "vector_index"; 32 Bson definition = new Document( 33 "fields", 34 Arrays.asList( 35 new Document("type", "vector") 36 .append("path", "plot_embedding") 37 .append("numDimensions", 1536) 38 .append("similarity", "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。
ファイルを実行してインデックスを作成します。
IDE でファイルを実行するか、コマンドラインからコマンドを実行してコードを実行します。
javac VectorIndex.java java VectorIndex
Successfully created a vector index named: [vector_index] It may take up to a minute for the index to build before you can query using it. Polling to confirm the index has completed building. vector_index index is ready to query
インデックスを定義します。
vector-index.js
という名前のファイルを作成します。次のコードをコピーして、ファイルに貼り付け、<connectionString>
プレースホルダー値を置き換えます。
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas deployment 4 const uri = "<connectionString>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 const database = client.db("sample_mflix"); 11 const collection = database.collection("embedded_movies"); 12 13 // define your Atlas Vector Search index 14 const index = { 15 name: "vector_index", 16 type: "vectorSearch", 17 definition: { 18 "fields": [ 19 { 20 "type": "vector", 21 "numDimensions": 1536, 22 "path": "plot_embedding", 23 "similarity": "euclidean" 24 }, 25 { 26 "type": "filter", 27 "path": "genres" 28 }, 29 { 30 "type": "filter", 31 "path": "year" 32 } 33 ] 34 } 35 } 36 37 // run the helper method 38 const result = await collection.createSearchIndex(index); 39 console.log(`New search index named ${result} is building.`); 40 41 // wait for the index to be ready to query 42 console.log("Polling to check if the index is ready. This may take up to a minute.") 43 let isQueryable = false; 44 while (!isQueryable) { 45 const cursor = collection.listSearchIndexes(); 46 for await (const index of cursor) { 47 if (index.name === result) { 48 if (index.queryable) { 49 console.log(`${result} is ready for querying.`); 50 isQueryable = true; 51 } else { 52 await new Promise(resolve => setTimeout(resolve, 5000)); 53 } 54 } 55 } 56 } 57 } finally { 58 await client.close(); 59 } 60 } 61 run().catch(console.dir);
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。
インデックスを定義します。
vector-index.py
という名前のファイルを作成します。次のコードをコピーして、ファイルに貼り付け、<connectionString>
プレースホルダー値を置き換えます。
1 from pymongo.mongo_client import MongoClient 2 from pymongo.operations import SearchIndexModel 3 import time 4 5 # Connect to your Atlas deployment 6 uri = "<connectionString>" 7 client = MongoClient(uri) 8 9 # Access your database and collection 10 database = client["sample_mflix"] 11 collection = database["embedded_movies"] 12 13 # Create your index model, then create the search index 14 search_index_model = SearchIndexModel( 15 definition={ 16 "fields": [ 17 { 18 "type": "vector", 19 "path": "plot_embedding", 20 "numDimensions": 1536, 21 "similarity": "euclidean" 22 }, 23 { 24 "type": "filter", 25 "path": "genres" 26 }, 27 { 28 "type": "filter", 29 "path": "year" 30 } 31 ] 32 }, 33 name="vector_index", 34 type="vectorSearch", 35 ) 36 37 result = collection.create_search_index(model=search_index_model) 38 print("New search index named " + result + " is building.") 39 # Wait for initial sync to complete 40 print("Polling to check if the index is ready. This may take up to a minute.") 41 predicate=None 42 if predicate is None: 43 predicate = lambda index: index.get("queryable") is True 44 45 while True: 46 indices = list(collection.list_search_indexes(result)) 47 if len(indices) and predicate(indices[0]): 48 break 49 time.sleep(5) 50 print(result + " is ready for querying.") 51 52 client.close()
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。
$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
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。
手順
AtlasGoClustersAtlas で、プロジェクトの ページにGoします。
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
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。
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。
Collectionsページに移動します。
クラスターの [Browse Collections] ボタンをクリックします。
Data Explorerが表示されます。
インデックス付きフィールドに対して Atlas Vector Search クエリを実行します。
次のクエリは、 $vectorSearch
パイプライン ステージを使用して、指定されたベクトル埋め込みに一致する映画を検索します。 クエリではplot_embedding
フィールドのベクトル埋め込みと同じada-002-text
埋め込みが使用されていることに注意してください。 クエリでは最大100
の最近傍の検索を指定し、結果を10
ドキュメントのみに制限します。 クエリでは、次のアクションを実行するための$project
ステージも指定します。
_id
フィールドを除外し、結果にはtitle
、genres
、plot
、およびyear
フィールドのみが含まれます。結果内の各ドキュメントのベクトル検索スコアを表示する
score
という名前のフィールドを追加します。
[</> Text] をクリックします。
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、
$and
集計パイプライン演算子を使用して、次の両方の条件に一致するドキュメントを検索します。genres
フィールドでフィルタリングして、drama
、western
、またはcrime
ジャンルではなく、action
、adventure
、またはfamily
ジャンルに含まれる映画を検索します。year
フィールドでフィルタリングして、1960
から2000
年(両端を含む)の間にリリースされた映画を検索します。
1 [ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding", 6 "filter": { 7 "$and": [{ 8 "genres": { 9 "$nin": ["Drama", "Western", "Crime"] , 10 "$in": ["Action", "Adventure", "Family"] 11 }, 12 }, { 13 "year": { 14 "$gte": 1960, 15 "$lte": 2000 16 } 17 }] 18 }, 19 "queryVector": 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20 "numCandidates": 200, 21 "limit": 10 22 } 23 }, 24 { 25 "$project": { 26 "_id": 0, 27 "title": 1, 28 "genres": 1, 29 "plot": 1, 30 "year": 1, 31 "score": { $meta: "vectorSearchScore" } 32 } 33 } 34 ] 1 year: 1993 2 plot: "A botched mid-air heist results in suitcases full of cash being search…" 3 genres: Array (3) 4 title: "Cliffhanger" 5 score: 0.7694521546363831 6 7 genres: Array (3) 8 title: "It's a Mad, Mad, Mad, Mad World" 9 year: 1963 10 score: 0.7638954520225525 11 plot: "The dying words of a thief spark a madcap cross-country rush to find s…" 12 13 score: 0.7538407444953918 14 year: 1991 15 plot: "A cat burglar is forced to steal Da Vinci works of art for a world dom…" 16 genres: Array (3) 17 title: "Hudson Hawk" 18 19 plot: "In 1997, when the US President crashes into Manhattan, now a giant max…" 20 genres: Array (2) 21 title: "Escape from New York" 22 year: 1981 23 score: 0.7487208843231201 24 25 title: "The Romanov Stones" 26 year: 1993 27 score: 0.7467736005783081 28 plot: "Patrick and Tony are hired by the wealthy gambler to steal the pricele…" 29 genres: Array (2) 30 31 plot: "An attempted robbery turns to be an unexpected recruitment when two un…" 32 genres: Array (3) 33 title: "Crime Busters" 34 year: 1977 35 score: 0.7437351942062378 36 37 plot: "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal …" 38 genres: Array (3) 39 title: "Three Kings" 40 year: 1999 41 score: 0.7425670623779297 42 43 plot: "A professional thief is hired by the FBI to steal a data tape from a c…" 44 genres: Array (3) 45 title: "Black Moon Rising" 46 year: 1986 47 score: 0.7397696375846863 48 49 plot: "A group of heavily armed hijackers board a luxury ocean liner in the S…" 50 genres: Array (3) 51 title: "Deep Rising" 52 year: 1998 53 score: 0.7392246127128601 54 55 year: 1964 56 score: 0.7357995510101318 57 plot: "A young man comes to the rescue of his girlfriend abducted by thieves …" 58 genres: Array (3) 59 title: "That Man from Rio" 次のクエリは、ベクトル埋め込みを使用して
plot_embedding
フィールドで string mongosを検索します。 クエリはada-002-text
埋め込みを使用します。これはplot_embedding
フィールドのベクトル埋め込みと同じです。 集計パイプライン 演算子と比較クエリ演算子を指定し、演算子を組み合わせて使用し、データをフィルタリングする方法を示します。フィルターでは、
$or
集計パイプライン演算子を使用して、次のいずれかの条件に一致する映画のドキュメントを検索します。genres
フィールドでフィルタリングして、crime
ジャンルに該当しない映画を検索します。year
フィールドでフィルタリングして、2015
年以前にリリースされた映画を検索し、genres
フィールドでフィルタリングしてaction
ジャンルの映画を検索します。
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18 "numCandidates": 200, 19 "limit": 10 20 } 21 }, 22 { 23 "$project": { 24 "_id": 0, 25 "title": 1, 26 "genres": 1, 27 "plot": 1, 28 "released": 1, 29 "score": { $meta: "vectorSearchScore" } 30 } 31 } 32 ] 1 title: "Ong-bak 2" 2 score: 0.7976737022399902 3 year: 2008 4 plot: "A young Thai boxer learns the skills and inner meaning of martial arts…" 5 genres: Array (1) 6 7 score: 0.7929348349571228 8 plot: "A handyman/martial arts master agrees to teach a bullied boy karate an…" 9 genres: Array (3) 10 title: "The Karate Kid" 11 year: 1984 12 13 plot: "A young martial artist embarks on an adventure, encountering other mar…" 14 genres: Array (3) 15 title: "Circle of Iron" 16 year: 1978 17 score: 0.7852014899253845 18 19 plot: "A lionized account of the life of the martial arts superstar." 20 genres: Array (3) 21 title: "Dragon: The Bruce Lee Story" 22 year: 1993 23 score: 0.7763040661811829 24 25 score: 0.7731232643127441 26 plot: "A martial arts instructor from the police force gets imprisoned after …" 27 genres: Array (2) 28 title: "Kung Fu Killer" 29 year: 2014 30 31 plot: "A young woman is trained by a martial arts specialist to become a prof…" 32 genres: Array (3) 33 title: "Naked Killer" 34 year: 1992 35 score: 0.7693743109703064 36 37 plot: "The life of the greatest karate master of a generation." 38 genres: Array (3) 39 title: "The Real Miyagi" 40 year: 2015 41 score: 0.768799901008606 42 43 plot: "At his new high school, a rebellious teen is lured into an underground…" 44 genres: Array (3) 45 title: "Never Back Down" 46 year: 2008 47 score: 0.768179714679718 48 49 score: 0.7679706811904907 50 plot: "Three young martial arts masters emerge from the back streets of Hong …" 51 genres: Array (2) 52 title: "Dragon Tiger Gate" 53 year: 2006 54 55 title: "Shaolin Soccer" 56 score: 0.7660527229309082 57 year: 2001 58 plot: "A young Shaolin follower reunites with his discouraged brothers to for…" 59 genres: Array (3)
注意
Pipeline Outputペインにはクエリの結果が表示されます。
クエリ結果を展開します。
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。
mongosh
でクラスターに接続します。
ターミナル ウィンドウでmongosh
を開き、クラスターに接続します。 接続の詳細な手順については、「 mongosh 経由での接続 」を参照してください。
インデックス フィールドに対してセマンティック検索クエリを実行します。
次のクエリは、 $vectorSearch
パイプライン ステージを使用して、指定されたベクトル埋め込みに一致する映画を検索します。 クエリではplot_embedding
フィールドのベクトル埋め込みと同じada-002-text
埋め込みが使用されていることに注意してください。 クエリでは最大100
の最近傍の検索を指定し、結果を10
ドキュメントのみに制限します。 クエリでは、次のアクションを実行するための$project
ステージも指定します。
_id
フィールドを除外し、結果にはtitle
、genres
、plot
、およびyear
フィールドのみが含まれます。結果内の各ドキュメントのベクトル検索スコアを表示する
score
という名前のフィールドを追加します。
次のクエリは、ベクトル埋め込みを使用して string履歴ハイストを検索しplot_embedding
。 クエリはada-002-text
埋め込みを使用します。これはplot_embedding
フィールドのベクトル埋め込みと同じです。 セマンティック検索を実行するドキュメントを事前にフィルタリングするために、フィールドごとに複数の比較クエリ演算子を指定します。
フィルターでは、 $and
集計パイプライン演算子を使用して、次の両方の条件に一致するドキュメントを検索します。
genres
フィールドでフィルタリングして、drama
、western
、またはcrime
ジャンルではなく、action
、adventure
、またはfamily
ジャンルに含まれる映画を検索します。year
フィールドでフィルタリングして、1960
から2000
年(両端を含む)の間にリリースされた映画を検索します。
1 db.embedded_movies.aggregate([ 2 { 3 "$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 "$lte": 2000 16 } 17 }] 18 }, 19 "queryVector": 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20 "numCandidates": 200, 21 "limit": 10 22 } 23 }, 24 { 25 "$project": { 26 "_id": 0, 27 "title": 1, 28 "genres": 1, 29 "plot": 1, 30 "year": 1, 31 "score": { $meta: "vectorSearchScore" } 32 } 33 } 34 ])
1 [ 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 8 }, 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 15 }, 16 { 17 year: 1991, 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 22 }, 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、 $or
集計パイプライン演算子を使用して、次のいずれかの条件に一致する映画のドキュメントを検索します。
genres
フィールドでフィルタリングして、crime
ジャンルに該当しない映画を検索します。year
フィールドでフィルタリングして、2015
年以前にリリースされた映画を検索し、genres
フィールドでフィルタリングしてaction
ジャンルの映画を検索します。
1 db.embedded_movies.aggregate([ 2 { 3 "$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": { "$eq": "Action" } 14 }] 15 }] 16 }, 17 "queryVector": 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18 "numCandidates": 200, 19 "limit": 10 20 } 21 }, 22 { 23 "$project": { 24 "_id": 0, 25 "title": 1, 26 "genres": 1, 27 "plot": 1, 28 "released": 1, 29 "score": { $meta: "vectorSearchScore" } 30 } 31 } 32 ])
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
コピーして、試行する事前フィルター演算子のコードをAtlasVectorSearchTutorial.go
ファイルに貼り付けます。
次のクエリは、 $vectorSearch
パイプライン ステージを使用して、指定されたベクトル埋め込みに一致する映画を検索します。 クエリではplot_embedding
フィールドのベクトル埋め込みと同じada-002-text
埋め込みが使用されていることに注意してください。 クエリでは最大100
の最近傍の検索を指定し、結果を10
ドキュメントのみに制限します。 クエリでは、次のアクションを実行するための$project
ステージも指定します。
_id
フィールドを除外し、結果にはtitle
、genres
、plot
、およびyear
フィールドのみが含まれます。結果内の各ドキュメントのベクトル検索スコアを表示する
score
という名前のフィールドを追加します。
次のクエリは、ベクトル埋め込みを使用して string履歴ハイストを検索しplot_embedding
。 クエリはada-002-text
埋め込みを使用します。これはplot_embedding
フィールドのベクトル埋め込みと同じです。 セマンティック検索を実行するドキュメントを事前にフィルタリングするために、フィールドごとに複数の比較クエリ演算子を指定します。
フィルターでは、 $and
集計パイプライン演算子を使用して、次の両方の条件に一致するドキュメントを検索します。
genres
フィールドでフィルタリングして、drama
、western
、またはcrime
ジャンルではなく、action
、adventure
、またはfamily
ジャンルに含まれる映画を検索します。year
フィールドでフィルタリングして、1960
から2000
年(両端を含む)の間にリリースされた映画を検索します。
1 package main 2 3 import ( 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 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(ctx, clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [1536]float64{ 31 -0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 0.010111088, 0.0074623227, 0.009555174, 0.008338705, -0.002240005, -0.0010603234, -0.004973792, 0.003391073, 0.021543289, 0.013341927, 0.0005980159, 0.010693162, 0.005336771, 0.016062634, 0.005768421, 0.005186347, 0.039790336, 0.0021942237, -0.0026275094, 0.010431555, 0.0042151334, -0.0050359233, 0.025768232, -0.021451725, 0.01833861, -0.01836477, -0.013433489, 0.030006256, -0.014793842, 0.017475309, 0.0020585153, -0.012975678, -0.017266022, -0.01593183, -0.014257549, 0.0010676811, -0.007887433, -0.0045911926, 0.00012303676, -0.0014976967, 0.03552615, 0.0065630507, -0.037435878, 0.011929252, -0.00939167, 0.016768971, 0.01223664, 0.007789331, -0.037200432, 0.013145722, 0.00896002, 0.021857215, 0.010333453, 0.021582529, -0.007089534, -0.007154935, -0.02485261, 0.0040254686, -0.00088864425, 0.023466095, -0.020719228, -0.006690584, -0.021006994, -0.018286288, 0.025545865, -0.0096598165, 0.008803056, -0.023021365, -0.040078104, 0.015408617, 0.017043658, -0.011242535, 0.0063537657, -0.026618453, 0.0071614753, -0.014623798, 0.00067322777, -0.00083427917, -0.028070368, 0.03714811, -0.004529061, 0.0054087127, 0.0028727653, 0.008384486, 0.010026066, -0.006190262, -0.0002493436, 0.0029953935, -0.026226042, -0.018417092, 0.009941043, 0.0036494094, -0.00982332, 0.013551212, 0.02574207, -0.0022645304, -0.0006004685, 0.012805633, -0.024303235, 0.008194821, -0.014179068, -0.02977081, 0.003095131, -0.0015941641, 0.029953934, 0.0052680993, 0.025388902, -0.031392768, -0.021386323, 0.014898485, 0.022419669, 0.00897964, 0.013243824, 0.006854088, 0.0066415328, -0.003839074, -0.01877026, 0.021216279, -0.015055449, -0.0015508354, 0.013211124, -0.008783435, 0.0052157775, -0.68938524, -0.01221702, -0.04125533, -0.016232677, 0.020039052, -0.0026422248, -0.0037050007, 0.0064682183, -0.0047579664, 0.0032749851, -0.0035382267, 0.031942144, -0.00035643874, -0.011628405, -0.043086577, -0.0196074, -0.0066088317, -0.014872325, 0.028331975, 0.010294212, -0.013930541, 0.031994462, -0.018626377, 0.017462227, 0.026343765, -0.010274592, 0.0046827546, -0.029430721, -0.011746128, 0.0024362097, 0.0023054064, 0.0027730279, -0.002406779, 0.003917556, 0.059436977, 0.008665713, -0.0018901062, 0.06037876, 0.017880797, 0.05185039, 0.0067102043, -0.020300657, 0.005604917, 0.018704858, 0.012073136, 0.0144145135, 0.012413224, -0.0074819434, 0.015801027, -0.0061412104, 0.008613391, -0.0039077457, -0.0036232488, 0.008469507, 0.014087505, 0.0124066835, 0.019267311, -0.002573553, 0.005055544, -0.009417831, -0.009103903, 0.011150973, -0.012046975, 0.0058567133, -0.0053727417, 0.018260127, -0.005588567, 0.015591742, 0.007495024, -0.02567667, 0.024211673, 0.021386323, -0.012890656, -0.016114954, 0.009515933, 0.009679437, 0.025532786, -0.0076454473, -0.02575515, 0.008319084, -0.0068410076, -0.017082898, -0.026173722, -0.0049901423, 0.01918883, -0.008646091, -0.031759016, 0.014820003, 0.011850771, 0.01836477, 0.012700991, -0.0011437106, 0.005058814, 0.0151993325, -0.0060692686, 0.027416352, 0.0037344315, 0.0013546307, 0.018325528, -0.03152357, -0.008809595, 0.014649959, -0.008345244, 0.0066415328, -0.005523165, 0.0043492066, -0.0015892589, 0.0048855, 0.034453563, -0.03837766, 0.0068410076, -0.0042151334, -0.0067429054, 0.0055689462, -0.011733048, -0.0212032, 0.016847452, -0.0022220195, 0.0059351954, -0.00449963, 0.02251123, -0.01020265, 0.023361452, -0.0032455544, 0.016180357, 0.0049443613, -0.0064747585, -0.03259616, 0.012321662, 0.020104453, 0.009954124, -0.019411195, 0.0048102876, -0.000392614, 0.012184318, 0.0044276887, 0.005634348, -0.020562263, 0.015722545, -0.005179807, -0.0067952266, 0.0027861083, 0.0024198592, -0.0020585153, 0.0018525004, -0.045100946, -0.010176489, -0.012956058, 0.0013497255, 0.0105361985, 0.003796563, -0.0106016, -0.013126101, 0.0050359233, 0.015003128, -0.0075800456, -0.015722545, -0.01755379, -0.00978408, -0.02940456, 0.017606111, 0.016612006, -0.016912855, 0.025441224, 0.0054741143, 0.00448001, 0.009470152, 0.015382457, -0.008332164, -0.019123428, 0.024564842, 0.016860534, 0.008286383, -0.007141855, 0.006559781, 0.016625088, -0.01840401, -0.011602244, -0.00489858, -0.0073184394, -0.008809595, -0.0018459603, -0.01629808, -0.005542786, 0.0064257076, 0.010379234, 0.014663039, 0.034872133, -0.013355007, 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0.018652538, -0.02443404, -0.025637427, 0.006497649, -0.015447859, 0.01917575, -0.016520444, -0.008678793, -0.021072397, 0.015840268, -0.006324335, 0.025925195, -0.03594472, 0.0384823, 0.01308032, 0.0054217926, 0.00448328, -0.027207067, -0.016847452, 0.0036003583, 0.01061468, -0.019816685, -0.004659864, 0.023387613, -0.005461034, 0.004326316, 0.0037278912, -0.007540805, 0.00860031, 0.0015524705, 0.020039052, -0.0028367946, 0.0049509015, 0.009162764, 0.009705598, 0.013982862, 0.004852799, 0.0061869915, -0.0083910255, 0.012975678, -0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353, -0.014126746, -0.009731758, -0.03398267, -0.000115883464, -0.0029725027, -0.024290156, 0.012864495, -0.00937859, -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
年以前にリリースされた映画を検索し、genres
フィールドでフィルタリングしてaction
ジャンルの映画を検索します。
1 package main 2 3 import ( 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 13 func 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, 0.013786677, 0.027235191, -0.0075046346, -0.015308415, -0.0031442728, 0.009136925, 0.010548109, 0.009455579, -0.0070624207, -0.001147806, 0.008928824, 0.011042348, -0.019314354, -0.0035702293, 0.019691538, 0.013929747, -0.00071615935, 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-0.0024793257, 0.0005182197, -0.008805265, -0.007589176, 0.013643608, 0.03685985, -0.024152698, 0.00400594, -0.0148401875, 0.01971755, 0.0002887805, -0.0050594504, -0.020120746, 0.00209564, 0.00084947393, 0.004688771, -0.013526551, 0.016140817, 0.024191717, 0.0020354858, 0.008200472, -0.014463005, -0.012453532, 0.0034596757, 0.0014843439, -0.0266369, 0.0069648735, -0.0044318964, -0.015620566, 0.0060154134, 0.018937172, -0.01588069, -0.0025882535, 0.03251575, 0.062118087, 0.017805625, -0.0098522715, -0.013968766, -0.016153824, 0.01641395, 0.0018794102, -0.019964669, 0.012290953, 0.024855038, -0.015334427, -0.012017821, 0.007647704, -0.01402079, -0.0008665447, 0.0069778794, 0.014007784, -0.009709203, -0.011022839, -0.017311385, -0.0050139283, -0.0033393675, 0.0025573636, 0.010463567, -0.028691897, -0.016752115, 0.008974346, 0.024373805, -0.003908393, -0.026142662, 0.015906705, -0.012440525, 0.006808798, 0.033998467, -0.006808798, 0.011764198, 0.0033913925, 0.003986431, 0.0067632757, 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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 }
を<connection-string>
Atlas 接続文字列 で置き換え、ファイルを保存します。
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。
ファイルをコンパイルして実行します。<a class=\" \" href=\" \" title=\" \"><svg xmlns=\" \" width=\" \" height=\" \" fill=\" \" viewbox=\" \" class=\" \" role=\" \" aria-label=\" \"><path fill=\" \" d=\" \"> <path fill=\" \" d=\" \">AtlasVectorSearchTutorial.go
go run AtlasVectorSearchTutorial.go
1 Title: Cliffhanger 2 Plot: A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains. 3 Genres: [Action Adventure Thriller] 4 Year: 1993 5 Score: 0.7694521546363831 6 7 Title: It's a Mad, Mad, Mad, Mad World 8 Plot: The dying words of a thief spark a madcap cross-country rush to find some treasure. 9 Genres: [Action Adventure Comedy] 10 Year: 1963 11 Score: 0.7638954520225525 12 13 Title: Hudson Hawk 14 Plot: A cat burglar is forced to steal Da Vinci works of art for a world domination plot. 15 Genres: [Action Adventure Comedy] 16 Year: 1991 17 Score: 0.7538407444953918 18 19 Title: Escape from New York 20 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. 21 Genres: [Action Sci-Fi] 22 Year: 1981 23 Score: 0.7487208843231201 24 25 Title: The Romanov Stones 26 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. 27 Genres: [Action Thriller] 28 Year: 1993 29 Score: 0.7467736005783081 30 31 Title: Crime Busters 32 Plot: An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store. 33 Genres: [Action Comedy Adventure] 34 Year: 1977 35 Score: 0.7437351942062378 36 37 Title: Three Kings 38 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. 39 Genres: [Action Adventure Comedy] 40 Year: 1999 41 Score: 0.7425670623779297 42 43 Title: Black Moon Rising 44 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 ... 45 Genres: [Action Sci-Fi Thriller] 46 Year: 1986 47 Score: 0.7397696375846863 48 49 Title: Deep Rising 50 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. 51 Genres: [Action Adventure Horror] 52 Year: 1998 53 Score: 0.7392246127128601 54 55 Title: That Man from Rio 56 Plot: A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues. 57 Genres: [Action Adventure Comedy] 58 Year: 1964 59 Score: 0.7357995510101318
go run AtlasVectorSearchTutorial.go
1 Title: Ong-bak 2 2 Plot: A young Thai boxer learns the skills and inner meaning of martial arts. 3 Genres: [Action] 4 Year: 2008 5 Score: 0.7976737022399902 6 7 Title: The Karate Kid 8 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. 9 Genres: [Action Drama Family] 10 Year: 1984 11 Score: 0.7929348349571228 12 13 Title: Circle of Iron 14 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. 15 Genres: [Action Adventure Fantasy] 16 Year: 1978 17 Score: 0.7852014899253845 18 19 Title: Dragon: The Bruce Lee Story 20 Plot: A lionized account of the life of the martial arts superstar. 21 Genres: [Action Biography Drama] 22 Year: 1993 23 Score: 0.7763040661811829 24 25 Title: Kung Fu Killer 26 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. 27 Genres: [Action Thriller] 28 Year: 2014 29 Score: 0.7731232643127441 30 31 Title: Naked Killer 32 Plot: A young woman is trained by a martial arts specialist to become a professional assassin. 33 Genres: [Action Crime Romance] 34 Year: 1992 35 Score: 0.7693743109703064 36 37 Title: The Real Miyagi 38 Plot: The life of the greatest karate master of a generation. 39 Genres: [Documentary Action History] 40 Year: 2015 41 Score: 0.768799901008606 42 43 Title: Never Back Down 44 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. 45 Genres: [Action Drama Sport] 46 Year: 2008 47 Score: 0.768179714679718 48 49 Title: Dragon Tiger Gate 50 Plot: Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice. 51 Genres: [Action Drama] 52 Year: 2006 53 Score: 0.7679706811904907 54 55 Title: Shaolin Soccer 56 Plot: A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage. 57 Genres: [Action Comedy Sport] 58 Year: 2001 59 Score: 0.7660527229309082
コピーして、試行する事前フィルター演算子のコードをAtlasVectorSearchTutorial.java
ファイルに貼り付けます。
次のクエリは、 $vectorSearch
パイプライン ステージを使用して、指定されたベクトル埋め込みに一致する映画を検索します。 クエリではplot_embedding
フィールドのベクトル埋め込みと同じada-002-text
埋め込みが使用されていることに注意してください。 クエリでは最大100
の最近傍の検索を指定し、結果を10
ドキュメントのみに制限します。 クエリでは、次のアクションを実行するための$project
ステージも指定します。
_id
フィールドを除外し、結果にはtitle
、genres
、plot
、およびyear
フィールドのみが含まれます。結果内の各ドキュメントのベクトル検索スコアを表示する
score
という名前のフィールドを追加します。
次のクエリは、ベクトル埋め込みを使用して string履歴ハイストを検索しplot_embedding
。 クエリはada-002-text
埋め込みを使用します。これはplot_embedding
フィールドのベクトル埋め込みと同じです。 セマンティック検索を実行するドキュメントを事前にフィルタリングするために、フィールドごとに複数の比較クエリ演算子を指定します。
フィルターでは、 $and
集計パイプライン演算子を使用して、次の両方の条件に一致するドキュメントを検索します。
genres
フィールドでフィルタリングして、drama
、western
、またはcrime
ジャンルではなく、action
、adventure
、またはfamily
ジャンルに含まれる映画を検索します。year
フィールドでフィルタリングして、1960
から2000
年(両端を含む)の間にリリースされた映画を検索します。
1 import static com.mongodb.client.model.Aggregates.*; 2 import static com.mongodb.client.model.Projections.*; 3 import static com.mongodb.client.model.search.SearchPath.fieldPath; 4 import static java.util.Arrays.asList; 5 6 import com.mongodb.client.MongoClient; 7 import com.mongodb.client.MongoClients; 8 import com.mongodb.client.MongoCollection; 9 import com.mongodb.client.MongoDatabase; 10 import com.mongodb.client.model.*; 11 import com.mongodb.client.model.search.VectorSearchOptions; 12 import java.util.Arrays; 13 import java.util.List; 14 import com.mongodb.client.model.search.FieldSearchPath; 15 import org.bson.Document; 16 import org.bson.conversions.Bson; 17 18 public 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, 0.017043658, 0.010111088, 0.0074623227, 0.009555174, 0.008338705, -0.002240005, -0.0010603234, -0.004973792, 0.003391073, 0.021543289, 0.013341927, 0.0005980159, 0.010693162, 0.005336771, 0.016062634, 0.005768421, 0.005186347, 0.039790336, 0.0021942237, -0.0026275094, 0.010431555, 0.0042151334, -0.0050359233, 0.025768232, -0.021451725, 0.01833861, -0.01836477, -0.013433489, 0.030006256, -0.014793842, 0.017475309, 0.0020585153, -0.012975678, -0.017266022, -0.01593183, -0.014257549, 0.0010676811, -0.007887433, -0.0045911926, 0.00012303676, -0.0014976967, 0.03552615, 0.0065630507, -0.037435878, 0.011929252, -0.00939167, 0.016768971, 0.01223664, 0.007789331, -0.037200432, 0.013145722, 0.00896002, 0.021857215, 0.010333453, 0.021582529, -0.007089534, -0.007154935, -0.02485261, 0.0040254686, -0.00088864425, 0.023466095, -0.020719228, -0.006690584, -0.021006994, -0.018286288, 0.025545865, -0.0096598165, 0.008803056, -0.023021365, -0.040078104, 0.015408617, 0.017043658, -0.011242535, 0.0063537657, -0.026618453, 0.0071614753, -0.014623798, 0.00067322777, -0.00083427917, -0.028070368, 0.03714811, -0.004529061, 0.0054087127, 0.0028727653, 0.008384486, 0.010026066, -0.006190262, -0.0002493436, 0.0029953935, -0.026226042, -0.018417092, 0.009941043, 0.0036494094, -0.00982332, 0.013551212, 0.02574207, -0.0022645304, -0.0006004685, 0.012805633, -0.024303235, 0.008194821, -0.014179068, -0.02977081, 0.003095131, -0.0015941641, 0.029953934, 0.0052680993, 0.025388902, -0.031392768, -0.021386323, 0.014898485, 0.022419669, 0.00897964, 0.013243824, 0.006854088, 0.0066415328, -0.003839074, -0.01877026, 0.021216279, -0.015055449, -0.0015508354, 0.013211124, -0.008783435, 0.0052157775, -0.68938524, -0.01221702, -0.04125533, -0.016232677, 0.020039052, -0.0026422248, -0.0037050007, 0.0064682183, -0.0047579664, 0.0032749851, -0.0035382267, 0.031942144, -0.00035643874, -0.011628405, -0.043086577, -0.0196074, -0.0066088317, -0.014872325, 0.028331975, 0.010294212, -0.013930541, 0.031994462, -0.018626377, 0.017462227, 0.026343765, -0.010274592, 0.0046827546, -0.029430721, -0.011746128, 0.0024362097, 0.0023054064, 0.0027730279, -0.002406779, 0.003917556, 0.059436977, 0.008665713, -0.0018901062, 0.06037876, 0.017880797, 0.05185039, 0.0067102043, -0.020300657, 0.005604917, 0.018704858, 0.012073136, 0.0144145135, 0.012413224, -0.0074819434, 0.015801027, -0.0061412104, 0.008613391, -0.0039077457, -0.0036232488, 0.008469507, 0.014087505, 0.0124066835, 0.019267311, -0.002573553, 0.005055544, -0.009417831, -0.009103903, 0.011150973, -0.012046975, 0.0058567133, -0.0053727417, 0.018260127, -0.005588567, 0.015591742, 0.007495024, -0.02567667, 0.024211673, 0.021386323, -0.012890656, -0.016114954, 0.009515933, 0.009679437, 0.025532786, -0.0076454473, -0.02575515, 0.008319084, -0.0068410076, -0.017082898, -0.026173722, -0.0049901423, 0.01918883, -0.008646091, -0.031759016, 0.014820003, 0.011850771, 0.01836477, 0.012700991, -0.0011437106, 0.005058814, 0.0151993325, -0.0060692686, 0.027416352, 0.0037344315, 0.0013546307, 0.018325528, -0.03152357, -0.008809595, 0.014649959, -0.008345244, 0.0066415328, -0.005523165, 0.0043492066, -0.0015892589, 0.0048855, 0.034453563, -0.03837766, 0.0068410076, -0.0042151334, -0.0067429054, 0.0055689462, -0.011733048, -0.0212032, 0.016847452, -0.0022220195, 0.0059351954, -0.00449963, 0.02251123, -0.01020265, 0.023361452, -0.0032455544, 0.016180357, 0.0049443613, -0.0064747585, -0.03259616, 0.012321662, 0.020104453, 0.009954124, -0.019411195, 0.0048102876, -0.000392614, 0.012184318, 0.0044276887, 0.005634348, -0.020562263, 0.015722545, -0.005179807, -0.0067952266, 0.0027861083, 0.0024198592, -0.0020585153, 0.0018525004, -0.045100946, -0.010176489, -0.012956058, 0.0013497255, 0.0105361985, 0.003796563, -0.0106016, -0.013126101, 0.0050359233, 0.015003128, -0.0075800456, -0.015722545, -0.01755379, -0.00978408, -0.02940456, 0.017606111, 0.016612006, -0.016912855, 0.025441224, 0.0054741143, 0.00448001, 0.009470152, 0.015382457, -0.008332164, -0.019123428, 0.024564842, 0.016860534, 0.008286383, -0.007141855, 0.006559781, 0.016625088, -0.01840401, -0.011602244, -0.00489858, -0.0073184394, -0.008809595, -0.0018459603, -0.01629808, -0.005542786, 0.0064257076, 0.010379234, 0.014663039, 0.034872133, -0.013355007, 0.027285548, 0.011654565, -0.004032009, 0.02323065, -0.02653997, -0.0009941043, 0.002946342, 0.010667001, 0.008345244, 0.018626377, 0.04821406, 0.031392768, 0.010281132, 0.026069079, 0.002735422, 0.01182461, -0.01593183, 0.006585941, -0.010071847, 0.024564842, -0.0025261368, 0.004293615, -0.0068606283, -0.0066448026, -0.0074100015, -0.0014347476, 0.021530207, -0.010418476, 0.018495573, -0.0034924455, -0.014165987, -0.004784127, -0.012472086, 0.004417878, -0.0030313642, -0.010084927, -0.010954768, 0.01508161, 0.0010047321, 0.0042347535, -0.03345946, -0.00027346043, 0.014793842, -0.019882087, 0.012772933, 0.021490967, 0.0031932332, 0.0093589695, 0.00090172456, 0.0048102876, 0.0070045115, -0.0045584915, 0.015840268, 0.024342475, -0.0091300635, 0.0039796876, 0.003796563, 0.025022654, -0.008103259, -0.025022654, 0.03021554, -0.008201361, -0.0070502926, 0.0011821339, 0.021072397, 0.004849529, -0.02495725, 0.012184318, 0.0019228071, -0.007226877, 0.020562263, 0.018861823, -0.0017593032, 0.01345965, 0.0022727058, 0.003023189, -0.026971621, -0.0030558899, 0.017723834, -0.01998673, -0.010608139, 0.011491061, -0.025179617, 0.0069652707, 0.003924096, 0.021177039, 0.0045650317, -0.0009973744, 0.007586586, -0.004032009, -0.008129419, -0.010091467, -0.04279881, 0.019790525, 0.01595799, 0.0044309585, -0.0033747226, -0.018665617, -0.012818714, -0.016206518, 0.014113666, -0.0020912162, 0.01427063, -0.020248337, -0.0112752365, -0.020588424, -0.011039791, 0.008744194, -0.015147011, 0.0022269245, -0.010438096, -0.0017772885, -0.028750544, -0.008861917, -0.016991336, 0.033668745, 0.034636687, 0.009888723, 0.0023953337, 0.006991431, -0.003346927, 0.003103306, -0.0044571194, 0.011249076, 0.0033779927, 0.00012446742, -0.0027027212, -0.025859794, -0.011942333, 0.02694546, 0.028227331, 0.0064289775, -0.03385187, -0.020719228, 0.00489531, 0.10663077, 0.041752383, -0.021700252, -0.008103259, 0.0049574412, -0.01675589, -0.020182934, -0.006585941, 0.007684688, -0.002859685, 0.027023941, 0.00856107, 0.0037017306, 0.016978256, 0.025885954, -0.010372694, 0.0025964435, 0.011706887, 0.021360163, -0.021674091, -0.024983412, 0.0034074234, 0.0032030435, 0.022262705, -0.01266829, -0.002249815, 0.032779284, -0.0034303141, -0.016101874, -0.005156916, -0.0212032, 0.005362931, 0.009077743, -0.013917461, -0.0017315074, 0.010980929, -0.019450437, 0.013865139, 0.028227331, -0.008757275, -0.0033649125, -0.012857955, 0.011039791, 0.009764459, 0.00029594224, -0.026317604, 0.025048813, 0.037749805, -0.025807472, -0.005425063, 0.021791814, -0.010012985, -0.00066995766, -0.016952096, 0.0031147513, -0.016598927, 0.0084368065, 0.004787397, -0.0064355177, 0.0015164997, -0.021216279, -0.023845425, 0.013969782, -0.011255615, 0.0042576445, -0.024250913, -0.009908343, -0.02289056, -0.023361452, -0.010987469, -0.013394248, 0.0032553647, -0.019018786, 0.021438645, 0.029587684, -0.010490417, 0.01263559, -0.018417092, -0.008731114, 0.01875718, -0.0072399573, -0.029090632, -0.017736914, -0.04031355, -0.019712042, 0.012772933, -0.030320182, -0.022341188, -0.02041838, 0.011752668, 0.028829027, -0.017043658, 0.024996493, 0.006334145, -0.0024263994, -0.0077370093, 0.017802317, 0.017396826, 0.030398665, 0.011464901, 0.03016322, -0.014558396, -0.0036690298, -0.009954124, -0.006703664, -0.00035705187, -0.014519156, 0.0075342646, -0.00896656, 0.040078104, 0.024420958, -0.016886694, -0.00092543266, -0.0017494928, 0.01672973, 0.016533526, 0.002648765, 0.0187441, -0.0055460557, 0.004735076, 0.03186366, 0.0003435628, 0.007495024, 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-0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353, -0.014126746, -0.009731758, -0.03398267, -0.000115883464, -0.0029725027, -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
年以前にリリースされた映画を検索し、genres
フィールドでフィルタリングしてaction
ジャンルの映画を検索します。
1 import static com.mongodb.client.model.Aggregates.*; 2 import static com.mongodb.client.model.Projections.*; 3 import static com.mongodb.client.model.search.SearchPath.fieldPath; 4 import static java.util.Arrays.asList; 5 6 import com.mongodb.client.MongoClient; 7 import com.mongodb.client.MongoClients; 8 import com.mongodb.client.MongoCollection; 9 import com.mongodb.client.MongoDatabase; 10 import com.mongodb.client.model.*; 11 import com.mongodb.client.model.search.VectorSearchOptions; 12 import java.util.List; 13 import com.mongodb.client.model.search.FieldSearchPath; 14 import org.bson.Document; 15 import org.bson.conversions.Bson; 16 17 public 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 }
を<connection-string>
Atlas 接続文字列 で置き換え、ファイルを保存します。
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。
ファイルをコンパイルして実行します。<a class=\" \" href=\" \" title=\" \"><svg xmlns=\" \" width=\" \" height=\" \" fill=\" \" viewbox=\" \" class=\" \" role=\" \" aria-label=\" \"><path fill=\" \" d=\" \"> <path fill=\" \" d=\" \">AtlasVectorSearchTutorial.java
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 }
コピーして、試行する事前フィルター演算子のサンプルクエリをatlas-vector-search-tutorial.js
ファイルに貼り付けます。
次のクエリは、 $vectorSearch
パイプライン ステージを使用して、指定されたベクトル埋め込みに一致する映画を検索します。 クエリではplot_embedding
フィールドのベクトル埋め込みと同じada-002-text
埋め込みが使用されていることに注意してください。 クエリでは最大100
の最近傍の検索を指定し、結果を10
ドキュメントのみに制限します。 クエリでは、次のアクションを実行するための$project
ステージも指定します。
_id
フィールドを除外し、結果にはtitle
、genres
、plot
、およびyear
フィールドのみが含まれます。結果内の各ドキュメントのベクトル検索スコアを表示する
score
という名前のフィールドを追加します。
次のクエリは、ベクトル埋め込みを使用して string履歴ハイストを検索しplot_embedding
。 クエリはada-002-text
埋め込みを使用します。これはplot_embedding
フィールドのベクトル埋め込みと同じです。 セマンティック検索を実行するドキュメントを事前にフィルタリングするために、フィールドごとに複数の比較クエリ演算子を指定します。
フィルターでは、 $and
集計パイプライン演算子を使用して、次の両方の条件に一致するドキュメントを検索します。
genres
フィールドでフィルタリングして、drama
、western
、またはcrime
ジャンルではなく、action
、adventure
、またはfamily
ジャンルに含まれる映画を検索します。year
フィールドでフィルタリングして、1960
から2000
年(両端を含む)の間にリリースされた映画を検索します。
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$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, 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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 } 68 run().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、 $or
集計パイプライン演算子を使用して、次のいずれかの条件に一致する映画のドキュメントを検索します。
genres
フィールドでフィルタリングして、crime
ジャンルに該当しない映画を検索します。year
フィールドでフィルタリングして、2015
年以前にリリースされた映画を検索し、genres
フィールドでフィルタリングしてaction
ジャンルの映画を検索します。
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$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 }, 43 '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, 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-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 } 70 run().catch(console.dir);
<connection-string>
クエリ内の を置き換えてから、ファイルを保存します。
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。
コレクションをクエリします。
次のコマンドを実行して、コレクションをクエリします。
node atlas-vector-search-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 }'
コピーして、試行する事前フィルター演算子のクエリをatlas-vector-search-tutorial.py
ファイルに貼り付けます。
次のクエリは、 $vectorSearch
パイプライン ステージを使用して、指定されたベクトル埋め込みに一致する映画を検索します。 クエリではplot_embedding
フィールドのベクトル埋め込みと同じada-002-text
埋め込みが使用されていることに注意してください。 クエリでは最大100
の最近傍の検索を指定し、結果を10
ドキュメントのみに制限します。 クエリでは、次のアクションを実行するための$project
ステージも指定します。
_id
フィールドを除外し、結果にはtitle
、genres
、plot
、およびyear
フィールドのみが含まれます。結果内の各ドキュメントのベクトル検索スコアを表示する
score
という名前のフィールドを追加します。
次のクエリは、ベクトル埋め込みを使用して string履歴ハイストを検索しplot_embedding
。 クエリはada-002-text
埋め込みを使用します。これはplot_embedding
フィールドのベクトル埋め込みと同じです。 セマンティック検索を実行するドキュメントを事前にフィルタリングするために、フィールドごとに複数の比較クエリ演算子を指定します。
フィルターでは、 $and
集計パイプライン演算子を使用して、次の両方の条件に一致するドキュメントを検索します。
genres
フィールドでフィルタリングして、drama
、western
、またはcrime
ジャンルではなく、action
、adventure
、またはfamily
ジャンルに含まれる映画を検索します。year
フィールドでフィルタリングして、1960
から2000
年(両端を含む)の間にリリースされた映画を検索します。
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 8 { 9 '$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, 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, 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-0.019816685, -0.004659864, 0.023387613, -0.005461034, 0.004326316, 0.0037278912, -0.007540805, 0.00860031, 0.0015524705, 0.020039052, -0.0028367946, 0.0049509015, 0.009162764, 0.009705598, 0.013982862, 0.004852799, 0.0061869915, -0.0083910255, 0.012975678, -0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353, -0.014126746, -0.009731758, -0.03398267, -0.000115883464, -0.0029725027, -0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783], 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 50 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 51 52 # print results 53 for 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、 $or
集計パイプライン演算子を使用して、次のいずれかの条件に一致する映画のドキュメントを検索します。
genres
フィールドでフィルタリングして、crime
ジャンルに該当しない映画を検索します。year
フィールドでフィルタリングして、2015
年以前にリリースされた映画を検索し、genres
フィールドでフィルタリングしてaction
ジャンルの映画を検索します。
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 8 { 9 '$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 '$eq': 'Action' 27 } 28 } 29 ] 30 } 31 ] 32 }, 33 'queryVector': [-0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 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0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914], 34 'numCandidates': 200, 35 'limit': 10 36 } 37 }, { 38 '$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 52 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 53 54 # print results 55 for i in result: 56 print(i)
<connection-string>
クエリ内の を置き換えてから、ファイルを保存します。
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。
コマンドを実行して、コレクションをクエリします。
python atlas-vector-search-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 }