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ハイブリッドAtlas Searchの実行方法

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。

  1. 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。

  2. sample_mflix.embedded_moviesコレクションに Atlas Search インデックスを作成します。

  3. 次の方法を使用して、$search クエリと$vectorSearch クエリの結果を組み合わせてクエリを実行します。

    • セマンティック ブースト

    • 因果ランクの統合

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

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。

ランキング方法

  • セマンティックに類似した結果を検索するには、$vectorSearch を実行します。

  • $search を実行して全文結果を取得すると同時に、$vectorSearch によって返されたドキュメントをブーストします。

  • $vectorSearch$search の結果が取得されます。

  • それぞれの結果セット内の各ドキュメントのランクを使用して、両方の結果セットをマージします。

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。

1.0/{document position in the results + constant value}

次に、検索方法ごとに異なる重み(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重みを掛けて重み付きの逆数ランクが計算されます。

weighted_reciprocal_rank = w x reciprocal_rank

検索機能

  • データをカテゴリにグループ化するためのファセットをサポートします。

  • searchAftersearchBefore を使用した結果のページ分割をサポートします。

$group などの後続の集計ステージで、 の結果のさらなる処理をサポートします。

出力

のドキュメントを含む単一の結果セット $search

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。

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 sample_mflix データ。

  • mongosh Atlas 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、Atlas Vector Search と Atlas Search インデックスを作成する

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。

1

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。

  1. 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。

  2. 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
  3. db.collection.createSearchIndex()メソッドを実行します。

    1db.embedded_movies.createSearchIndex(
    2 "hybrid-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

次のインデックス定義は、コレクション内のすべての動的にインデックス可能なフィールドを自動的に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。

  1. 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。

  2. 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
  3. db.collection.createSearchIndex()メソッドを実行します。

    1db.embedded_movies.createSearchIndex(
    2 "hybrid-full-text-search",
    3 "search",
    4 {
    5 "mappings": {
    6 "dynamic": true
    7 }
    8 }
    9);

このセクションでは、それぞれsample_mflix.embedded_movies plot_embeddingplot$vectorSearch$search 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表す コレクション内のデータをクエリする方法を示します。このクエリは、同じドキュメントの$search スコアを使用して全文スコアをブーストすることで、$vectorSearch クエリの関連性を向上させます。

1
  1. query-embeddings.jsという名前のファイルを作成します。

    touch query-embeddings.js
  2. 次の埋め込みをコピーしてファイルに貼り付けます。

    QUERY_EMBEDDING = 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  3. ファイルを保存して閉じます。

2

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。

3

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
4

query-embeddings.jsファイルを mongosh にロードします。

load('<path-to-query-embeddings.js-file>');
5
1vectorWeight = 1;
2vectorCutoff = 0.7;
3numResults = 20;
4numCandidates = numResults * 10;
5
6// Execute the vector search
7vectorResults = db.embedded_movies.aggregate([
8 {
9 $vectorSearch: {
10 index: "hybrid-vector-search",
11 path: "plot_embedding",
12 queryVector: QUERY_EMBEDDING,
13 numCandidates: numCandidates,
14 limit: numResults
15 }
16 },
17 {
18 $project: {
19 _id: 1,
20 title: 1,
21 plot: 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
33vectorScoresMap = {};
34vectorResults.forEach(result => {
35 vectorScoresMap[result._id] = result.vectorScore * vectorWeight;
36});
37
38// Execute the text search with boosting based just on ID
39textResults = db.embedded_movies.aggregate([
40 {
41 $search: {
42 index: "hybrid-full-text-search",
43 compound: {
44 should: [
45 {
46 text: {
47 query: "star wars",
48 path: "plot"
49 }
50 },
51 ...Object.keys(vectorScoresMap).map(id => ({
52 equals: {
53 path: "_id",
54 value: id,
55 score: { boost: { value: vectorScoresMap[id] } }
56 }
57 }))
58 ]
59 }
60 }
61 },
62 {
63 $limit: 20
64 },
65 {
66 $project: {
67 _id: 1,
68 title: 1,
69 plot: 1,
70 score: { $meta: "searchScore" }
71 }
72 }
73]).toArray();
[
{
_id: ObjectId('573a139df29313caabcfa90b'),
plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
title: 'Message from Space',
score: 4.401333808898926
},
{
_id: ObjectId('573a1397f29313caabce6f53'),
plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
title: 'Message from Space',
score: 4.401333808898926
},
{
_id: ObjectId('573a1398f29313caabce9851'),
plot: 'Johnathan Cabot is a champion gymnast. In the tiny, yet savage, country of Parmistan, there is a perfect spot for a "star wars" site. For the US to get this site, they must compete in the ...',
title: 'Gymkata',
score: 4.284864902496338
},
{
_id: ObjectId('573a1394f29313caabcdf65b'),
plot: 'A fantastic tale of war, love, family and ambition set in the midst of the Japanese Civil Wars of the sixteenth century.',
title: 'Ugetsu',
score: 2.858368396759033
},
{
_id: ObjectId('573a13a4f29313caabd1137f'),
plot: "A producer's film is endangered when his star walks off, so he decides to digitally create an actress to substitute for the star, becoming an overnight sensation that everyone thinks is a real person.",
title: 'S1m0ne',
score: 2.842216730117798
},
{
_id: ObjectId('573a139af29313caabcefe79'),
plot: 'An all star baseball player becomes the unhealthy focus of a down on his luck salesman.',
title: 'The Fan',
score: 2.6598150730133057
},
{
_id: ObjectId('573a13a3f29313caabd0c992'),
plot: 'The vampire Lestat becomes a rock star whose music wakes up the queen of all vampires.',
title: 'Queen of the Damned',
score: 2.6598150730133057
},
{
_id: ObjectId('573a13a8f29313caabd1da86'),
plot: 'During the Napoleonic Wars, a brash British captain pushes his ship and crew to their limits in pursuit of a formidable French war vessel around South America.',
title: 'Master and Commander: The Far Side of the World',
score: 2.643709897994995
},
{
_id: ObjectId('573a139af29313caabcf0e7d'),
plot: 'A puppeteer discovers a portal that leads literally into the head of the movie star, John Malkovich.',
title: 'Being John Malkovich',
score: 2.612804889678955
},
{
_id: ObjectId('573a13b6f29313caabd4614f'),
plot: 'A former pop star who now writes commercial jingles for a living experiences a mid-life crisis.',
title: 'The Good Night',
score: 2.612804889678955
},
{
_id: ObjectId('573a13cbf29313caabd7fbc9'),
plot: 'Bertrand Tavernier is in top form with this gripping, superbly mounted drama set against the savage Catholic/Protestant wars that ripped France apart in the 16th century. Based on a novella...',
title: 'The Princess of Montpensier',
score: 2.4938809871673584
},
{
_id: ObjectId('573a139bf29313caabcf2d8e'),
plot: 'Updated version of the classic Rodgers and Hammerstein musical of the classic fairy-tale, with an all-star, multi-racial cast.',
title: 'Cinderella',
score: 2.44028377532959
},
{
_id: ObjectId('573a1399f29313caabcec853'),
plot: 'An action movie star researching a role is allowed to tag along with a hardboiled New York policeman, who finds him superficial and irritating.',
title: 'The Hard Way',
score: 2.325138807296753
},
{
_id: ObjectId('573a13b4f29313caabd3f550'),
plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.',
title: 'Divergence',
score: 2.325138807296753
},
{
_id: ObjectId('573a13aff29313caabd2fe20'),
plot: 'An intimate story set during the 1860s in which a young Irish woman Sarah and her family find themselves on both sides of the turbulent wars between British and Maori during the British colonization of New Zealand.',
title: 'River Queen',
score: 2.298485040664673
},
{
_id: ObjectId('573a13aef29313caabd2df95'),
plot: 'Story of two sisters that grew up in a small Serbian village in the beginning of the 1930s. The village is torn up by wars and years long blood oath. There are no men left in the village. ...',
title: 'Tears for Sale',
score: 2.268857479095459
},
{
_id: ObjectId('573a13b8f29313caabd4d8e1'),
plot: "Thomas Michael, Paolo Mancini, Chris Klein and Joe Mantegna star in director Matthiew Klinck's holiday-themed comedy concerning two downsized Easter bunnies whose friendship starts to ...",
title: 'Hank and Mike',
score: 2.254227876663208
},
{
_id: ObjectId('573a139af29313caabcf124d'),
plot: 'As the Clone Wars near an end, the Sith Lord Darth Sidious steps out of the shadows, at which time Anakin succumbs to his emotions, becoming Darth Vader and putting his relationships with Obi-Wan and Padme at risk.',
title: 'Star Wars: Episode III - Revenge of the Sith',
score: 2.2399840354919434
},
{
_id: ObjectId('573a13c1f29313caabd633ac'),
plot: "Black Dynamite is the greatest African-American action star of the 1970s. When his only brother is killed by The Man it's up to him to find justice.",
title: 'Black Dynamite',
score: 2.1875147819519043
},
{
_id: ObjectId('573a13dbf29313caabdaf598'),
plot: "Just as Clint Eastwood's star-making spaghetti Western A Fistful of Dollars was inspired by Akira Kurosawa's Yojimbo, Japanese-Korean filmmaker Sang-il Lee (Villain) has decided to ...",
title: 'Unforgiven',
score: 2.1875147819519043
}
]

サンプルクエリは、 結果スコアに基づいて$search $vectorSearch結果をブーストし、最も関連性の高いドキュメントを返します。セマンティック ブーストでは、次の手順を実行します。

  1. $vectorSearch クエリを実行します。

    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。

  2. 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。

  3. $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。

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。

1
  1. query-embeddings.jsという名前のファイルを作成します。

  2. 次の埋め込みをコピーして、ファイルに貼り付けます。

  3. ファイルを保存して閉じます。

2

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。

3

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
4

埋め込みをロードするには、次のコマンドを実行します。

load('<path-to-query-embeddings.js');

埋め込みが正常にロードされたことを確認するには、次のコマンドを実行します。

STAR_WARS_EMBEDDING.length
1536
5
1var vector_weight = 0.1;
2var full_text_weight = 0.9;
3db.embedded_movies.aggregate([
4 {
5 "$vectorSearch": {
6 "index": "hybrid-vector-search",
7 "path": "plot_embedding",
8 "queryVector": 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77 }
78 ]
79 }
80 }
81 },
82 {
83 "$project": {
84 "fts_score": 1,
85 "_id": "$docs._id",
86 "title": "$docs.title"
87 }
88 }
89 ]
90 }
91 },
92 {
93 "$group": {
94 "_id": "$_id",
95 "title": {"$first": "$title"},
96 "vs_score": {"$max": "$vs_score"},
97 "fts_score": {"$max": "$fts_score"}
98 }
99 },
100 {
101 "$project": {
102 "_id": 1,
103 "title": 1,
104 "vs_score": {"$ifNull": ["$vs_score", 0]},
105 "fts_score": {"$ifNull": ["$fts_score", 0]}
106 }
107 },
108 {
109 "$project": {
110 "score": {"$add": ["$fts_score", "$vs_score"]},
111 "_id": 1,
112 "title": 1,
113 "vs_score": 1,
114 "fts_score": 1
115 }
116 },
117 {"$sort": {"score": -1}},
118 {"$limit": 10}
119])
{
_id: new ObjectId('573a13c0f29313caabd62f62'),
title: 'Star Wars: The Clone Wars',
vs_score: 0.0015625,
fts_score: 0.015,
score: 0.0165625
}
{
_id: new ObjectId('573a1397f29313caabce68f6'),
title: 'Star Wars: Episode IV - A New Hope',
vs_score: 0.0016666666666666668,
fts_score: 0.014754098360655738,
score: 0.016420765027322405
}
{
_id: new ObjectId('573a139af29313caabcf0f5f'),
title: 'Star Wars: Episode I - The Phantom Menace',
vs_score: 0.0016393442622950822,
fts_score: 0.014516129032258065,
score: 0.016155473294553146
}
{
_id: new ObjectId('573a1397f29313caabce77d9'),
title: 'Star Wars: Episode V - The Empire Strikes Back',
vs_score: 0.0016129032258064516,
fts_score: 0.014285714285714285,
score: 0.015898617511520736
}
{
_id: new ObjectId('573a1397f29313caabce8cdb'),
title: 'Star Wars: Episode VI - Return of the Jedi',
vs_score: 0.0015873015873015873,
fts_score: 0.0140625,
score: 0.015649801587301587
}
{
_id: new ObjectId('573a139af29313caabcf124d'),
title: 'Star Wars: Episode III - Revenge of the Sith',
vs_score: 0.0013698630136986301,
fts_score: 0.013846153846153847,
score: 0.015216016859852476
}
{
_id: new ObjectId('573a139af29313caabcf1258'),
title: 'Star Wars: Episode II - Attack of the Clones',
vs_score: 0.0014925373134328358,
fts_score: 0.013636363636363637,
score: 0.015128900949796473
}
{
_id: new ObjectId('573a1397f29313caabce6f53'),
title: 'Message from Space',
vs_score: 0.0015384615384615387,
fts_score: 0,
score: 0.0015384615384615387
}
{
_id: new ObjectId('573a139df29313caabcfa90b'),
title: 'Message from Space',
vs_score: 0.0015151515151515154,
fts_score: 0,
score: 0.0015151515151515154
}
{
_id: new ObjectId('573a13d9f29313caabda92ff'),
title: 'Guardians of the Galaxy',
vs_score: 0.0014705882352941176,
fts_score: 0,
score: 0.0014705882352941176
}

行 117 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: new ObjectId('573a13b0f29313caabd34a3e'),
title: 'Cowboys & Aliens',
vs_score: 0.0012658227848101266,
fts_score: 0,
score: 0.0012658227848101266
}
{
_id: new ObjectId('573a13a3f29313caabd0ec59'),
title: 'Planet of the Apes',
vs_score: 0.001282051282051282,
fts_score: 0,
score: 0.001282051282051282
}
{
_id: new ObjectId('573a139bf29313caabcf3d4b'),
title: 'Planet of the Apes',
vs_score: 0.001298701298701299,
fts_score: 0,
score: 0.001298701298701299
}
{
_id: new ObjectId('573a1397f29313caabce7509'),
title: 'Starcrash',
vs_score: 0.0013157894736842105,
fts_score: 0,
score: 0.0013157894736842105
}
{
_id: new ObjectId('573a13b0f29313caabd33d15'),
title: 'Zathura: A Space Adventure',
vs_score: 0.0013333333333333335,
fts_score: 0,
score: 0.0013333333333333335
}
{
_id: new ObjectId('573a1398f29313caabce8d67'),
title: 'Space Raiders',
vs_score: 0.0013513513513513514,
fts_score: 0,
score: 0.0013513513513513514
}
{
_id: new ObjectId('573a1398f29313caabce90bd'),
title: 'The Ewok Adventure',
vs_score: 0.001388888888888889,
fts_score: 0,
score: 0.001388888888888889
}
{
_id: new ObjectId('573a1398f29313caabce9091'),
title: 'Dune',
vs_score: 0.0014084507042253522,
fts_score: 0,
score: 0.0014084507042253522
}
{
_id: new ObjectId('573a13d4f29313caabd9887f'),
title: 'Abiogenesis',
vs_score: 0.0014285714285714286,
fts_score: 0,
score: 0.0014285714285714286
}
{
_id: new ObjectId('573a13d5f29313caabd9c312'),
title: 'Guardians of the Galaxy',
vs_score: 0.0014492753623188406,
fts_score: 0,
score: 0.0014492753623188406
}

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。

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

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