MongoDB ベクトル検索とMongoDB Search クエリをハイブリッド検索として使用すると、統合結果が得られます。
このチュートリアルでは、映画の詳細を含むsample_mflix.embedded_moviesコレクションでハイブリッド検索を実行する方法を説明します。 具体的には、このチュートリアルでは次の手順について説明します。
plot_embedding_voyage_4_largeフィールドにMongoDBベクトル検索インデックスを作成します。このフィールドには、映画のあらすじの要約を表現するベクトル埋め込み（Voyage AIのvoyage-4-large埋め込みモデルを使用して作成された2048次元のもの）が含まれています。sample_mflix.embedded_moviesコレクションにMongoDB Searchインデックスを作成します。Run a query to combine the results from a
$vectorSearchquery and a$searchquery by using the following techniques:セマンティック ブースト
因果ランクの統合
スコアフュージョン
ハイブリッド検索を使用する理由
ハイブリッド検索とは、同じクエリ条件に対して全文検索やセマンティック検索などの異なる検索方法を集約したものです。 全文検索はクエリ用語の完全一致を見つけるのに効果的ですが、ドキュメントに完全なクエリ用語が含まれていない場合でも、セマンティック検索ではセマンティック類似ドキュメントを識別できるという追加の利点があります。 これにより、シノニムとコンテキストが類似する一致も、両方の検索方法の合計結果に含まれるようになります。
逆に、データセットで使用されるのと同じコンテキストで埋め込みモデルの訓練で考慮されることが予想されない場合は、ベクトル検索に次のことがメリットをもたらす可能性があります。全文検索と組み合わせて使用します。
クエリごとに、各検索方法の重みを設定することもできます。 全文検索結果とセマンティック検索結果がクエリに最も関連し、適切であるかどうかに基づいて、クエリごとのその検索方法の重みを増やすことができます。
ハイブリッド検索を実現するには、次の手法を適用できます。
手法 | セマンティック ブースト | 因果ランクの統合 | 相対スコア融合 | |||
目的 | ベクトル検索に一致するドキュメントを優先することで、全文検索結果の精度を向上させます。非常に大きな結果セットを検索する場合に便利です。 | 結果内のドキュメントの逆ランクを計算することにより、異なる検索メソッドの結果を単一のランク付けされた結果セットに結合します。 | 結果内のドキュメントのスコアを計算することにより、異なる検索メソッドの結果を単一のランク付けされた結果セットに結合します。 | |||
ランキング方法 |
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|
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スコアリング方法 | ベクトル検索の結果に一致するドキュメントの全文結果スコアを、重み付きベクトル検索スコアでブーストします。 | 次の式を使用してスコアを計算します。 次に、検索方法ごとに異なる重み（ | 次の式を使用してスコアを計算します。 | |||
検索機能 | Supports further processing of results in subsequent aggregation stages such as | Supports further processing of results in subsequent aggregation stages such as | ||||
出力 | のドキュメントを含む単一の結果セット | 両方の検索方法のドキュメントを含む単一の結果セット。 | 両方の検索方法のドキュメントを含む単一の結果セット。 |
前提条件
始める前に、以下のものを用意してください。
MongoDBバージョン v8.0 以上を搭載した Atlas クラスター。
注意
MongoDB Search とMongoDB ベクトル検索インデックスの両方を保存し、パフォーマンス クエリを実行するのに十分なメモリがあることを Atlas クラスターに確認してください。
The sample_mflix data loaded into your Atlas cluster.
mongoshAtlas クラスターでクエリを実行するには、次の手順に従います。Project Data Access Adminプロジェクトにアクセスして、 MongoDB ベクトル検索とMongoDB Search インデックスを作成します。
MongoDB ベクトル検索とMongoDB Search インデックスの作成
このセクションでは、 sample_mflix.embedded_moviesコレクションのフィールドに次のインデックスを作成する方法について説明します。
MongoDB ベクトル検索インデックス（そのフィールドに対してベクトルクエリを実行中ための
plot_embedding_voyage_4_largeフィールドの MongoDB Vector Searchインデックス。フィールドに対して全文検索を実行するために、ダイナミックにインデックス可能なすべてのフィールドをダイナミックにインデックスする MongoDB Search インデックス。
1
Connect to the cluster using mongosh.
To learn more, see Connect to a Cluster via mongosh.
3
MongoDB ベクトル検索インデックスを定義します。
次のコマンドを実行します。このインデックス定義は、コレクションをクエリするときに、plot_embedding_voyage_4_largeフィールドをMongoDB ベクトル検索フィールドとしてインデックス化します。
1 db.embedded_movies.createSearchIndex( 2 "hybrid-vector-search", 3 "vectorSearch", 4 { 5 "fields": [ 6 { 7 "type": "vector", 8 "path": "plot_embedding_voyage_4_large", 9 "numDimensions": 2048, 10 "similarity": "dotProduct" 11 } 12 ] 13 } 14 );
ハイブリッド検索クエリの実行
This section demonstrates how to query the data in the sample_mflix.embedded_movies collection for charming animal in the plot_embedding_voyage_4_large and fullplot fields by using the $vectorSearch and $search pipeline stages, respectively. The query enhances relevancy of $search queries by boosting the full-text scores using $vectorSearch scores for the same documents.
セマンティック ブースト クエリ
1
埋め込み用のファイルを作成します。
query-embeddings.jsという名前のファイルを作成します。touch query-embeddings.js 次の埋め込みをコピーしてファイルに貼り付けます。
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2
Connect to your cluster using mongosh.
Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect to a Cluster via mongosh.
3
sample_mflix データベースを使用します。
Run the following command at mongosh prompt:
use sample_mflix
switched to db sample_mflix
4
5
embedded_moviesコレクションに対して次のMongoDB Search クエリを実行します。
1 vectorWeight = 1; 2 vectorCutoff = 0.7; 3 numResults = 20; 4 numCandidates = numResults * 10; 5 6 // Execute the vector search 7 vectorResults = db.embedded_movies.aggregate([ 8 { 9 $vectorSearch: { 10 "index": "hybrid-vector-search", 11 "path": "plot_embedding_voyage_4_large", 12 "queryVector": CHARMING_ANIMAL_EMBEDDING, 13 "numCandidates": numCandidates, 14 "limit": numResults 15 } 16 }, 17 { 18 $project: { 19 "_id": 1, 20 "title": 1, 21 "fullplot": 1, 22 "vectorScore": { $meta: "vectorSearchScore" } 23 } 24 }, 25 { 26 $match: { 27 "vectorScore": { $gte: vectorCutoff } 28 } 29 } 30 ]).toArray(); 31 32 // Create a map for fast lookup 33 vectorScoresMap = {}; 34 vectorResults.forEach(result => { 35 vectorScoresMap[result._id] = result.vectorScore * vectorWeight; 36 }); 37 38 // Execute the text search with boosting based just on ID 39 textResults = db.embedded_movies.aggregate([ 40 { 41 $search: { 42 "index": "hybrid-full-text-search", 43 "compound": { 44 "should": [ 45 { 46 "text": { 47 "query": "charming animal", 48 "path": "fullplot", 49 "fuzzy": {} 50 } 51 }, 52 ...Object.keys(vectorScoresMap).map(id => ({ 53 "equals": { 54 "path": "_id", 55 "value": id, 56 "score": { boost: { value: vectorScoresMap[id] } } 57 } 58 })) 59 ] 60 } 61 } 62 }, 63 { 64 $limit: 20 65 }, 66 { 67 $project: { 68 "_id": 1, 69 "title": 1, 70 "fullplot": 1, 71 "score": { $meta: "searchScore" } 72 } 73 } 74 ]).toArray();
クエリについて
The sample query boosts the $search results based on $vectorSearch result scores to return the most relevant documents. For semantic boosting, you perform the following steps:
Run the
$vectorSearchquery.指定された制限以上の候補数を指定して、考慮する必要がある値は、オーバーリクエスト値を使用します。オーバーリクエストを使用すると、次のことが実行できます。
考慮する必要がある多数のドキュメント プールを初期検索に含めます。
後続のステージでスコアリングの計算を調整します。
クエリは、次の計算を使用して、ベクトル検索中に考慮する候補の数を決定します。
numCandidates = limit * over request 結果は、最小スコアしきい値（
vectorCutoff）に基づいてフィルタリングされます。ベクトル検索スコアをスケーリングして、ドキュメントのスコアリングとランキングへのベクトル検索結果の影響を調整します。
vectorWeightパラメーターを使用して重みを設定し、ベクトル検索スコアにvectorWeightを掛けます。これによりベクトル検索結果の重要性が制御され、最もセマンティックに関連するドキュメントのみが考慮されるようになります。$searchクエリを実行し、ベクトル検索の結果に一致するドキュメントのスコアをブーストします。
相互ランクの統合クエリ
You can run a query that retrieves the sorted search results from the semantic search and the full-text search, and assigns a reciprocal rank score to the documents in the results based on their position in the results array. To learn more about the reciprocal rank fusion technique and how to run a reciprocal rank fusion query, see Perform Hybrid Vector and Full-Text Search.
相対スコアフュージョンクエリ
You can run a query that retrieves the scored search results from the semantic and full-text searches and applies the sigmoid expression to normalize the score before combining the results from the pipelines into a single aggregated score for each document. To learn more about the score fusion technique and how to run a score fusion query, see Perform Hybrid Vector and Full-Text Search.