The $rerank aggregation stage reorders input documents using Voyage AI reranking models and returns the same documents sorted by relevance to the query. The $rerank stage can appear anywhere in an aggregation pipeline.
You don't need to create an index to run $rerank queries. However, MongoDB recommends using $rerank after a $vectorSearch, $search, $rankFusion, or $scoreFusion stage, which require creating a MongoDB Vector Search or MongoDB Search index on your collection.
このクイックスタートでは、 sample_mflix データセットに対するクエリで $rerank集計ステージを使用して、Atlas でネイティブ 再ランク付けを有効にして使用する方法を示します。
前提条件
始める前に、次のタスクを完了してください。
sample_mflix データセット をクラスターにロードします。
詳細については、「 サンプル データのロード 」を参照してください。
の使用に関する互換性と要件の詳細については、「$rerank 考慮事項 」を参照してください。rerank-3 rerank-3-liteモデルと モデルでは、 MongoDB9.0 以降を実行中専有クラスターが必要です。詳細については、「 要件 」を参照してください。
手順
ネイティブ再ランクを開始するには、次の手順に従ってください。
1
ネイティブ再ランク付けを有効にします。
Atlas で、Project Settings ページに移動します。
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。
サイドバーで、 アイコンを Project Overview の横にあるをクリックします。
[ Project Settings ]ページが表示されます。
Native Reranking セクションまでスクロールし、Enable Native Reranking を On に切り替えます。
[Confirm] をクリックします。
2
ネイティブ再ランク付けクエリを実行します。
次のクエリでは、$rerank ステージを使用して、「格闘技」のクエリに最も関連の深い映画の上位 10 作品を検索します。
sample_mflixデータベースに切り替えます:use sample_mflix ネイティブ再ランク付けクエリを実行します。
次のクエリでは、
rerank-2.5モデルを使用して、$rerankステージを使用して、格闘技のクエリに最も関連の深い映画を返します。クエリでは、格闘技の訓練と大会に関する映画が優先されます。次のパイプライン ステージを使用します。パイプラインステージ説明string 型の
fullplotフィールドを持つドキュメントのみを含むようにドキュメントをフィルターします。ドキュメントを
releasedフィールドの降順にソートして、確定的な順序を確保します。rerank-2.5リランカー モデルを使用して、クエリに一致するようにドキュメントを再配置します。numDocsToRerankパラメータは、リランカーの対象となるドキュメントの最大数を指定します。pathパラメータは、リランカーに使用するフィールドを指定します。ドキュメントに
rerankScoreという名前のフィールドを追加します。出力を10ドキュメントに制限します。
結果のドキュメントから、
title、fullplot、rerankScore以外のすべてのフィールドを除外します。1 db.embedded_movies.aggregate([ 2 { 3 "$match": { 4 "fullplot": { "$exists": true, "$type": "string" } 5 } 6 }, 7 { 8 "$sort": { "released": -1 } 9 }, 10 { 11 "$rerank": { 12 "model": "rerank-2.5", 13 "query": { 14 "text": "martial arts; prioritize films about martial arts training and tournaments" 15 }, 16 "path": "fullplot", 17 "numDocsToRerank": 100 18 } 19 }, 20 { 21 "$addFields": { 22 "rerankScore": { "$meta": "score" } 23 } 24 }, 25 { "$limit": 10 }, 26 { 27 "$project": { 28 "_id": 0, 29 "title": 1, 30 "fullplot": 1, 31 "rerankScore": 1 32 } 33 } 34 ])
3
$vectorSearch と $search を使用してネイティブ リランキング クエリを実行中ためのインデックスを作成します。
MongoDB recommends using $rerank after a $vectorSearch, $search, $rankFusion, or $scoreFusion stage. To do so, create a MongoDB Vector Search and MongoDB Search index on your collection to run the $vectorSearch and $search stages.
このクイックスタートの $vectorSearch ステージと $search ステージを使用するサンプルクエリを試すには、コレクションにMongoDB ベクトル検索とMongoDB Searchインデックスを作成する必要があります。
インデックスを作成するには、次の手順を実行します。
サイドバーの Search & Vector Search をクリックします。
[Create Index] をクリックします。
インデックスの設定を開始します。
インデックスタイプとして Vector Search を選択します。
インデックスの名前として「
vector_index」と入力します。sample_mflix.embedded_moviesコレクションを選択します。エディターの種類として JSON Editor を選択します。
Next をクリックし、次のインデックス定義を JSON エディターに貼り付けます。
``plot_embedding_voyage_4_large`` フィールドのインデックス。{ "fields": [ { "type": "vector", "path": "plot_embedding_voyage_4_large", "numDimensions": 2048, "similarity": "dotProduct" } ] } [Next をクリックし、[Create Index] をクリックします。
インデックスを作成するには、次の手順を実行します。
サイドバーの Search & Vector Search をクリックします。
[Create Index] をクリックします。
インデックスの設定を開始します。
インデックスタイプとして Search を選択します。
インデックスの名前として「
search_index」と入力します。sample_mflix.embedded_moviesコレクションを選択します。エディターの種類として JSON Editor を選択します。
Next をクリックし、次のインデックス定義を JSON エディターに貼り付けます。
動的にインデックス可能なフィールドの動的インデックス。{ "mappings": { "dynamic": true } } [Next をクリックし、[Create Index] をクリックします。
4
ネイティブ 再ランク付けで $vectorSearch クエリと $search クエリを実行する準備をします。
クエリで使用する埋め込みを保存するには、
embeddings.jsという名前のファイルを作成します。次の埋め込みをコピーしてファイルに貼り付けます。
クエリ用語 "martial arts" の埋め込み。MARTIAL_ARTS_EMBEDDINGS=[0.02218237891793251, -0.007462590467184782, -0.01691064052283764, 0.0072914301417768, -0.011365045793354511, -0.045186325907707214, -6.846413452876732e-05, -0.0034916705917567015, 0.009995763190090656, 0.025742514058947563, -0.002481824718415737, -0.0029268416110426188, 0.0023106643930077553, -0.04135233536362648, -0.019717669114470482, -0.024510158225893974, -0.008729176595807076, -0.001048356993123889, 0.01033808384090662, 0.008900336921215057, -0.00797607097774744, 0.03258892521262169, 0.00766798248514533, 0.010406548157334328, 0.04107847809791565, -0.020128455013036728, -0.03833991289138794, 0.018348386511206627, -0.01533596497029066, 0.042995475232601166, 0.020813096314668655, -0.0016260230913758278, -0.004364588297903538, -0.011159653775393963, -0.03436899557709694, 0.010817333124577999, -0.0008900337270461023, -0.012118151411414146, -0.0156782865524292, 0.04381704330444336, -0.013350505381822586, 0.003851107321679592, 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-0.015062108635902405, 0.037518344819545746, 0.036696773022413254, -0.008421087637543678, 0.027111796662211418, -0.0035772507544606924, -0.012871257029473782, 0.01889610104262829, -0.01369282603263855, -0.026701010763645172, 0.027111796662211418, -0.022319307550787926, -0.020128455013036728, 0.008489551953971386, -0.011159653775393963, -0.014172075316309929, -0.01615753397345543, 0.029028791934251785]; sample_mflixデータベースに切り替えます:use sample_mflix 埋め込みを
mongoshにロードします。load('embeddings.js');
5
ネイティブ 再ランク付けで $vectorSearch と $search を実行します。
The following queries use the $rerank stage after a $vectorSearch and $search stage to find the top ten movies that are most relevant to the query for martial arts.
次のクエリでは、$vectorSearch ステージを使用して、武術のクエリに最も関連の映画を照合します。クエリでは、$rerank ステージを使用してドキュメントを再編成し、戦闘スタイルと戦闘の振付けに集中します。次のパイプライン ステージを使用します。
パイプラインステージ | 説明 |
|---|---|
| |
結果のドキュメントから、 | |
string 型の | |
| |
ドキュメントに | |
出力を10ドキュメントに制限します。 | |
結果のドキュメントから、 |
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding_voyage_4_large", 6 "queryVector": MARTIAL_ARTS_EMBEDDINGS, 7 "numCandidates": 100, 8 "limit": 100 9 } 10 }, 11 { 12 "$project": { 13 "_id": 0, 14 "title": 1, 15 "fullplot": 1, 16 "vectorScore": { "$meta": "vectorSearchScore" } 17 } 18 }, 19 { 20 "$match": { 21 "fullplot": { "$exists": true, "$type": "string" } 22 } 23 }, 24 { 25 "$rerank": { 26 "model": "rerank-2.5", 27 "query": { 28 "text": "martial arts; focus on combat styles and fight choreography" 29 }, 30 "path": "fullplot", 31 "numDocsToRerank": 100 32 } 33 }, 34 { 35 "$addFields": { 36 "rerankScore": { "$meta": "score" } 37 } 38 }, 39 { "$limit": 10 }, 40 { 41 "$project": { 42 "_id": 0, 43 "title": 1, 44 "fullplot": 1, 45 "vectorScore": 1, 46 "rerankScore": 1 47 } 48 } 49 ])
次のクエリは、$search ステージを使用して、クエリ テキスト パフォーマンス の全文検索を実行します。次に、クエリは $rerank ステージを使用してドキュメントの順序を変更し、プロットのプロットを優先します。次のパイプラインステージを使用します。
パイプラインステージ | 説明 |
|---|---|
クエリテキスト「武術」の全文検索を実行します。 | |
| |
結果のドキュメントから、 | |
string 型の | |
| |
ドキュメントに | |
出力を10ドキュメントに制限します。 | |
結果のドキュメントから、 |
1 db.embedded_movies.aggregate([ 2 { 3 "$search": { 4 "index": "search_index", 5 "text": { 6 "query": "martial arts", 7 "path": "fullplot" 8 } 9 } 10 }, 11 { 12 "$limit": 100 13 }, 14 { 15 "$project": { 16 "_id": 0, 17 "title": 1, 18 "fullplot": 1, 19 "searchScore": { "$meta": "searchScore" } 20 } 21 }, 22 { 23 "$match": { 24 "fullplot": { "$exists": true, "$type": "string" } 25 } 26 }, 27 { 28 "$rerank": { 29 "model": "rerank-2.5", 30 "query": { 31 "text": "martial arts; prioritize movie plots about martial arts competition and training" 32 }, 33 "path": "fullplot", 34 "numDocsToRerank": 100 35 } 36 }, 37 { 38 "$addFields": { 39 "rerankScore": { "$meta": "score" } 40 } 41 }, 42 { "$limit": 10 }, 43 { 44 "$project": { 45 "_id": 0, 46 "title": 1, 47 "fullplot": 1, 48 "searchScore": 1, 49 "rerankScore": 1 50 } 51 } 52 ])
追加リソース
ネイティブなリランキングの詳細については、次のリソースを参照してください。