Atlas Vector Search と Atlas Search を使用したハイブリッド検索の実行
項目一覧
Atlas Vector Search と Atlas Search クエリをハイブリッド検索に結合して、統合された結果を得ることができます。
このチュートリアルでは、映画の詳細が含まれる sample_mflix.embedded_moviesコレクションに対して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フィールドに Atlas Vector Search インデックスを作成します。 このフィールドには、映画のプロットの概要を表すベクトル埋め込みが含まれています。sample_mflix.embedded_moviesコレクションのtitleフィールドに Atlas Search インデックスを作成します。 このフィールドには、映画名がテキスト string として含まれています。相互ランク統合を使用するクエリを実行して、
plot_embeddingフィールドに対する$vectorSearchクエリとtitleフィールドに対する$searchクエリの結果を組み合わせます。
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。
因果ランク統合とは
因果ランク統合とは、次のアクションを実行して、セマンティック検索や全文検索などの異なる検索メソッドからの結果を単一の結果セットに結合する手法です。
結果内のドキュメントの逆ランクを計算します。
各検索結果内のランク付けされたドキュメントごとに、まず定数
60を持つドキュメントのランク（r）を追加してスコアを調整します（rank_constant）、次に1を の合計で割ります。結果内のドキュメントの逆ランクの場合、rとrank_constant。reciprocal_rank = 1 / ( r + rank_constant ) 各検索方法に異なる重み（
w）を適用して、その検索方法の重要性を高めます。 各ドキュメントについて、重みをドキュメントの逆数ランクで乗算して重みを含む、重み付きランクを計算します。weighted_reciprocal_rank = w x reciprocal_rank 結果内のドキュメントのランク付けスコアと重み付けスコアを組み合わせます。
すべての検索結果にわたるドキュメントごとに、ドキュメントの単一のスコアに対して計算された逆数ランクを追加します。
結果内のドキュメントの合計スコアで結果を並べ替えます。
結果内のドキュメントの単一の組み合わせたランク付けリストの結果全体の合計スコアに基づいて、結果内のドキュメントをソートします。
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 v 6.0.11 、または v 7.0.2以降の Atlas クラスター。
注意
Atlas クラスターに、Atlas Search インデックスと Atlas Vector Search 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確認してください。
Atlas クラスターにロードされたサンプル データ。
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。
Project Data Access Adminプロジェクトにアクセスして、Atlas Vector Search と Atlas Search インデックスを作成する
Atlas Vector Search と Atlas 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。
plot_embeddingフィールドの Atlas Vector Search 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するため）。titleフィールドに対して全文検索を実行するための Atlas Search インデックス。
手順
1
Atlas Vector Search インデックスを定義します。
次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 フィールドを Atlas Vector Search フィールドとしてインデックス化します。
mongosh v 2.1.2以降を使用してコレクションに Atlas Vector Search インデックスを作成するには、次の手順を実行します。
mongoshを使用して Atlas クラスターに接続します。詳しくは、「
mongosh経由で接続 」を参照してください。インデックスを作成するコレクションを含むデータベースに切り替えます。
use sample_mflix switched to db sample_mflix db.collection.createSearchIndex()メソッドを実行します。1 db.embedded_movies.createSearchIndex( 2 "rrf-vector-search", 3 "vectorSearch", 4 { 5 "fields": [ 6 { 7 "type": "vector", 8 "path": "plot_embedding", 9 "numDimensions": 1536, 10 "similarity": "dotProduct" 11 } 12 ] 13 } 14 );
2
Atlas Search インデックスを定義します。
次のインデックス定義では、フィールドをクエリするためのstring型としてtitleフィールドをインデックス化します。
mongosh v2.1.2 以降を使用してコレクションの Atlas Search インデックスを作成するには、次の手順を実行します。
mongoshを使用して Atlas クラスターに接続します。詳しくは、「
mongosh経由で接続 」を参照してください。インデックスを作成するコレクションを含むデータベースに切り替えます。
use sample_mflix switched to db sample_mflix db.collection.createSearchIndex()メソッドを実行します。1 db.embedded_movies.createSearchIndex( 2 "rrf-full-text-search", 3 "search", 4 { 5 "mappings": { 6 "dynamic": false, 7 "fields": { 8 "title": [{ 9 "type": "string" 10 }] 11 } 12 } 13 } 14 );
1
環境設定
Node.js プロジェクトを初期化します。
ターミナルで次のコマンドを実行して、
hybrid-search-mongodbという名前の新しいディレクトリを作成し、プロジェクトを初期化します。mkdir hybrid-search-mongodb cd hybrid-search-mongodb npm init -y 依存関係をインストールしてインポートします。
次のコマンドを実行します:
npm install mongodb package.jsonファイルを更新します。プロジェクトの
package.jsonファイルで、次の例に示すようにtypeフィールドを指定し、ファイルを保存します。{ "name": "hybrid-search-mongodb", "type": "module", ... .envファイルを作成します。プロジェクトに、Atlas 接続文字列を保存するための
.envファイルを作成します。ATLAS_CONNECTION_STRING = "<connection-string>"; <api-key>と<connection-string>プレースホルダの値を Atlas クラスタの SRV 接続文字列に置き換えます。接続文字列には、次の形式を使用する必要があります。mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net 注意
Node.js の最低バージョン要件
Node.js v20.x は、
--env-fileオプションを導入しました。古いバージョンの Node.js を使用している場合は、dotenvパッケージをプロジェクトに追加するか、別の方法を使用して環境変数を管理します。
2
Atlas Vector Search インデックスを定義します。
次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 フィールドを Atlas Vector Search フィールドとしてインデックス化します。
avs-index.jsというファイルを作成し、次のコードをそのファイルに貼り付けます。import { MongoClient } from 'mongodb'; // Connect to your Atlas deployment const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); async function run() { try { const database = client.db("sample_mflix"); const collection = database.collection("embedded_movies"); // Define your Atlas Vector Search index const index = { name: "rrf-vector-search", type: "vectorSearch", definition: { "fields": [ { "type": "vector", "numDimensions": 1536, "path": "plot_embedding", "similarity": "dotProduct" } ] } } // Call the method to create the index const result = await collection.createSearchIndex(index); console.log(result); } finally { await client.close(); } } run().catch(console.dir); 次のコマンドを実行して、コードを実行します。
node --env-file=.env avs-index.js Atlas がインデックスを作成するのに数分かかる場合があります。Atlas はインデックスが使用可能になるとメールを送信します。Atlas UI では、新しく作成したインデックスは Atlas Search タブに表示されます。インデックスを作成する間、Status フィールドには Build in Progress と表示されます。インデックスの作成が完了すると、Status フィールドの表示は Active になります。
3
Atlas Search インデックスを定義します。
次のインデックス定義では、フィールドをクエリするためのstring型としてtitleフィールドをインデックス化します。
fts-index.jsというファイルを作成し、次のコードをそのファイルに貼り付けます。import { MongoClient } from 'mongodb'; // Connect to your Atlas deployment const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); async function run() { try { const database = client.db("sample_mflix"); const collection = database.collection("embedded_movies"); // Define your Atlas Search index const index = { name: "rrf-full-text-search", type: "search", definition: { "mappings": { "dynamic": false, "fields": { "title": [{ "type": "string" }] } } } } // Call the method to create the index const result = await collection.createSearchIndex(index); console.log(result); } finally { await client.close(); } } run().catch(console.dir); 次のコマンドを実行して、コードを実行します。
node --env-file=.env fts-index.js Atlas がインデックスを作成するのに数分かかる場合があります。Atlas はインデックスが使用可能になるとメールを送信します。Atlas UI では、新しく作成されたインデックスは Atlas Search タブに表示されます。インデックスを作成する間、Status フィールドには Build in Progress と表示されます。インデックスの作成が完了すると、Status フィールドの表示は Active になります。
セマンティック検索と全文検索クエリの実行を組み合わせたセマンティック検索クエリと全文検索クエリの実行
このセクションでは、sample_mflix.embedded_movies plot_embeddingtitle$vectorSearch} パイプライン$search ステージと パイプライン ステージを使用して、 フィールドと フィールドの スターターム をチェックする方法と、両方のステージの各ドキュメントのスコアを組み合わせて再試行する方法を示します。結果内のドキュメントをソートします。これにより、両方の検索で表示されるドキュメントが、結合された結果の上位に表示されるようになります。
手順
1
mongosh を使用して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。
2
sample_mflix データベースを使用します。
mongoshプロンプトで次のコマンドを実行します。
use sample_mflix
switched to db sample_mflix
3
embedded_movies コレクションに対して、以下のAtlas Searchクエリを実行します。
1 var vector_weight = 0.1; 2 var full_text_weight = 0.9; 3 db.embedded_movies.aggregate([ 4 { 5 "$vectorSearch": { 6 "index": "rrf-vector-search", 7 "path": "plot_embedding", 8 "queryVector": 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81 }, 82 { 83 "$project": { 84 "fts_score": 1, 85 "_id": "$docs._id", 86 "title": "$docs.title" 87 } 88 } 89 ] 90 } 91 }, 92 { 93 "$group": { 94 "_id": "$title", 95 "vs_score": {"$max": "$vs_score"}, 96 "fts_score": {"$max": "$fts_score"} 97 } 98 }, 99 { 100 "$project": { 101 "_id": 1, 102 "title": 1, 103 "vs_score": {"$ifNull": ["$vs_score", 0]}, 104 "fts_score": {"$ifNull": ["$fts_score", 0]} 105 } 106 }, 107 { 108 "$project": { 109 "score": {"$add": ["$fts_score", "$vs_score"]}, 110 "_id": 1, 111 "title": 1, 112 "vs_score": 1, 113 "fts_score": 1 114 } 115 }, 116 {"$sort": {"score": -1}}, 117 {"$limit": 10} 118 ])
[ { _id: 'Star Wars: Episode IV - A New Hope', vs_score: 0.0016666666666666668, fts_score: 0, score: 0.0016666666666666668 }, { _id: 'Star Wars: Episode I - The Phantom Menace', vs_score: 0.0016393442622950822, fts_score: 0, score: 0.0016393442622950822 }, { _id: 'Star Wars: Episode V - The Empire Strikes Back', vs_score: 0.0016129032258064516, fts_score: 0, score: 0.0016129032258064516 }, { _id: 'Star Wars: Episode VI - Return of the Jedi', vs_score: 0.0015873015873015873, fts_score: 0, score: 0.0015873015873015873 }, { _id: 'Star Wars: The Clone Wars', vs_score: 0.0015625, fts_score: 0, score: 0.0015625 }, { _id: 'Message from Space', vs_score: 0.0015384615384615387, fts_score: 0, score: 0.0015384615384615387 }, { _id: 'Star Wars: Episode II - Attack of the Clones', vs_score: 0.0014925373134328358, fts_score: 0, score: 0.0014925373134328358 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 } ]
行 116 上の score の値を 1 に置き換えて結果を昇順にソートすると、Atlas Vector Search は次の結果を返します。
[ { _id: 'Cowboys & Aliens', vs_score: 0.0012658227848101266, fts_score: 0, score: 0.0012658227848101266 }, { _id: 'Planet of the Apes', vs_score: 0.001298701298701299, fts_score: 0, score: 0.001298701298701299 }, { _id: 'Starcrash', vs_score: 0.0013157894736842105, fts_score: 0, score: 0.0013157894736842105 }, { _id: 'Zathura: A Space Adventure', vs_score: 0.0013333333333333335, fts_score: 0, score: 0.0013333333333333335 }, { _id: 'Space Raiders', vs_score: 0.0013513513513513514, fts_score: 0, score: 0.0013513513513513514 }, { _id: 'Star Wars: Episode III - Revenge of the Sith', vs_score: 0.0013698630136986301, fts_score: 0, score: 0.0013698630136986301 }, { _id: 'The Ewok Adventure', vs_score: 0.001388888888888889, fts_score: 0, score: 0.001388888888888889 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 } ]
1
結合されたクエリ パイプラインを定義します。
combined-query.jsというファイルを作成し、次のコードをそのファイルに貼り付けます。
1 import { MongoClient } from 'mongodb'; 2 3 async function run() { 4 // Connect to your Atlas cluster 5 const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); 6 7 try { 8 await client.connect(); 9 const database = client.db("sample_mflix"); 10 const collection = database.collection("embedded_movies"); 11 12 const vectorWeight = 0.1; 13 const fullTextWeight = 0.9; 14 const pipeline = [ 15 { 16 "$vectorSearch": { 17 "index": "rrf-vector-search", 18 "path": "plot_embedding", 19 "queryVector": 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20 "numCandidates": 100, 21 "limit": 20 22 } 23 }, { 24 "$group": { 25 "_id": null, 26 "docs": {"$push": "$$ROOT"} 27 } 28 }, { 29 "$unwind": { 30 "path": "$docs", 31 "includeArrayIndex": "rank" 32 } 33 }, { 34 "$addFields": { 35 "vs_score": { 36 "$multiply": [ 37 vectorWeight, { 38 "$divide": [ 39 1.0, { 40 "$add": ["$rank", 60] 41 } 42 ] 43 } 44 ] 45 } 46 } 47 }, { 48 "$project": { 49 "vs_score": 1, 50 "_id": "$docs._id", 51 "title": "$docs.title" 52 } 53 }, { 54 "$unionWith": { 55 "coll": "movies", 56 "pipeline": [ 57 { 58 "$search": { 59 "index": "rrf-full-text-search", 60 "phrase": { 61 "query": "star wars", 62 "path": "title" 63 } 64 } 65 }, { 66 "$limit": 20 67 }, { 68 "$group": { 69 "_id": null, 70 "docs": {"$push": "$$ROOT"} 71 } 72 }, { 73 "$unwind": { 74 "path": "$docs", 75 "includeArrayIndex": "rank" 76 } 77 }, { 78 "$addFields": { 79 "fts_score": { 80 "$multiply": [ 81 fullTextWeight, { 82 "$divide": [ 83 1.0, { 84 "$add": ["$rank", 60] 85 } 86 ] 87 } 88 ] 89 } 90 } 91 }, 92 { 93 "$project": { 94 "fts_score": 1, 95 "_id": "$docs._id", 96 "title": "$docs.title" 97 } 98 } 99 ] 100 } 101 }, { 102 "$group": { 103 "_id": "$title", 104 "vs_score": {"$max": "$vs_score"}, 105 "fts_score": {"$max": "$fts_score"} 106 } 107 }, { 108 "$project": { 109 "_id": 1, 110 "title": 1, 111 "vs_score": {"$ifNull": ["$vs_score", 0]}, 112 "fts_score": {"$ifNull": ["$fts_score", 0]} 113 } 114 }, { 115 "$project": { 116 "score": {"$add": ["$fts_score", "$vs_score"]}, 117 "_id": 1, 118 "title": 1, 119 "vs_score": 1, 120 "fts_score": 1 121 } 122 }, 123 {"$sort": {"score": -1}}, 124 {"$limit": 10} 125 ]; 126 127 // run pipeline 128 const result = collection.aggregate(pipeline); 129 130 for await (const doc of result) { 131 console.log(doc); 132 } 133 } catch (err) { 134 console.log(err.stack); 135 } 136 finally { 137 await client.close(); 138 } 139 } 140 run().catch(console.dir);
2
コードを実行して、embedded_movies コレクションに対してクエリを実行します。
node --env-file=.env combined-query.js
[ { _id: 'Star Wars: Episode IV - A New Hope', vs_score: 0.0016666666666666668, fts_score: 0, score: 0.0016666666666666668 }, { _id: 'Star Wars: Episode I - The Phantom Menace', vs_score: 0.0016393442622950822, fts_score: 0, score: 0.0016393442622950822 }, { _id: 'Star Wars: Episode V - The Empire Strikes Back', vs_score: 0.0016129032258064516, fts_score: 0, score: 0.0016129032258064516 }, { _id: 'Star Wars: Episode VI - Return of the Jedi', vs_score: 0.0015873015873015873, fts_score: 0, score: 0.0015873015873015873 }, { _id: 'Star Wars: The Clone Wars', vs_score: 0.0015625, fts_score: 0, score: 0.0015625 }, { _id: 'Message from Space', vs_score: 0.0015384615384615387, fts_score: 0, score: 0.0015384615384615387 }, { _id: 'Star Wars: Episode II - Attack of the Clones', vs_score: 0.0014925373134328358, fts_score: 0, score: 0.0014925373134328358 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 } ]
行 123 上の score の値を 1 に置き換えて結果を昇順にソートすると、Atlas Vector Search は次の結果を返します。
[ { _id: 'Cowboys & Aliens', vs_score: 0.0012658227848101266, fts_score: 0, score: 0.0012658227848101266 }, { _id: 'Planet of the Apes', vs_score: 0.001298701298701299, fts_score: 0, score: 0.001298701298701299 }, { _id: 'Starcrash', vs_score: 0.0013157894736842105, fts_score: 0, score: 0.0013157894736842105 }, { _id: 'Zathura: A Space Adventure', vs_score: 0.0013333333333333335, fts_score: 0, score: 0.0013333333333333335 }, { _id: 'Space Raiders', vs_score: 0.0013513513513513514, fts_score: 0, score: 0.0013513513513513514 }, { _id: 'Star Wars: Episode III - Revenge of the Sith', vs_score: 0.0013698630136986301, fts_score: 0, score: 0.0013698630136986301 }, { _id: 'The Ewok Adventure', vs_score: 0.001388888888888889, fts_score: 0, score: 0.001388888888888889 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 } ]
クエリについて
サンプル クエリは、セマンティック検索と全文検索からソートされた検索結果を検索し、結果配列内のドキュメントの位置に基づいて結果内のドキュメントに逆数ランク スコアを割り当てます。 逆ランクスコアは、次の式を使用して計算されます。
1.0/{document position in the results + constant value}
次に、このクエリは各ドキュメントの両方の検索からのスコアを追加し、合計スコアに基づいてドキュメントをランク付けし、ドキュメントをソートして 1 つの結果を返します。
クエリ変数
サンプル クエリでは、スコアに重みを追加するために次の変数を定義し、数値が低いほど重みが高くなります。
vector_weightfull_text_weight
重み付けされた逆ランクスコアは、次の式を使用して計算されます。
weight x reciprocal rank
クエリ ステージ
サンプル クエリでは、次のパイプライン ステージを使用して コレクションに対してセマンティック検索を実行し、結果内のドキュメントの逆ランクを検索します。
クエリの queryVectorフィールドにベクトル埋め込みとして指定された stringスター警告をplot_embeddingフィールドで検索します。 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。 このクエリでは、最大100個の最近傍の検索を指定し、結果を20ドキュメントのみに制限します。 このステージでは、セマンティック検索からソートされたドキュメントが結果に返されます。 | |
セマンティック検索の結果内のすべてのドキュメントを docsという名前のフィールドでグループ化します。 | |
docsフィールド内のドキュメントの配列を展開し、結果配列内のドキュメントの位置をrankという名前のフィールドに保存します。 | |
結果内の各ドキュメントの逆ランクスコアを含む vs_scoreという名前の新しいフィールドを追加します。 ここで、逆数ランク スコアは、 1.0を60 rankとランク定数の合計で割った値を計算します。 次に、 vector_weightの重みと逆数ランク スコアを乗算して重み付けされた逆数ランクを計算します。 | |
結果には次のフィールドのみが含まれます。
|
サンプル クエリでは、 $unionWithステージを使用してコレクションに対してテキスト検索を実行し、結果内のドキュメントの逆ランクを取得します。
titleフィールドにstar warsというタームが含まれる映画を検索します。 このステージでは、全文検索からソートされたドキュメントが結果に返されます。 | |
出力を 20の結果のみに制限します。 | |
全文検索のすべてのドキュメントを docsという名前のフィールドでグループ化します。 | |
docsフィールド内のドキュメントの配列を展開し、結果配列内のドキュメントの位置をrankという名前のフィールドに保存します。 | |
結果内の各ドキュメントの逆ランクスコアを含む fts_scoreという名前の新しいフィールドを追加します。 ここで、逆数ランク スコアは、 1.0を60 rankとランク定数の合計で割った値を計算します。 次に、 full_text_weightの重みと逆数ランク スコアを乗算して重み付けされた逆数ランクを計算します。 | |
結果には次のフィールドのみが含まれます。
|
サンプル クエリでは、次のステージを使用してセマンティック検索とテキスト検索の結果を組み合わせて、結果内のドキュメントの単一のランク付けされたリストを返します。
ビデオで学ぶ
Atlas Search の全文検索とベクトル検索を組み合わせて 1 つのマージされた結果セットを返すハイブリッド検索クエリを紹介するアプリケーションのデモをご覧ください。 アプリケーションは、ランク統合アルゴリズムを使用して作成されたマージされたセットを返すために、相対的スコア統合（RSF）と整数ランク統合（RRF）を実装します。
所要時間: 2.43 分