Atlas Vector Search

項目一覧

Overview

Atlas Vector Search インデックス
ベクトル検索集計ステージ
Atlas Search クエリの例
基本的なベクトル検索クエリ
ベクトル検索スコア
ベクトル検索オプション
詳細情報
API ドキュメント

Overview

このガイドでは、Atlas ベクトル検索機能を使用して、ドキュメントに対して検索を実行する方法を学習できます。 Rustドライバーでは、集計フレームワークを使用して Atlas ベクトル検索クエリを実行できます。集計の実行の詳細については、集計ガイドをご覧ください。

注意

配置の互換性

Atlas ベクトル検索機能は、 MongoDB Atlasクラスターに接続する場合にのみ使用できます。この機能は、自己管理型の配置では使用できません。

Atlas ベクトル検索の詳細については、 Atlas ベクトル検索の概要を参照してください。

注意

Atlas Search

ドキュメントに対して高度な全文検索を実行するには、Atlas Search 機能を使用できます。この機能について詳しくは、「 Atlas Search の概要」を参照してください。

Atlas Vector Search インデックス

Atlas ベクトル検索クエリを実行する前に、コレクションに Atlas ベクトル検索インデックスを作成する必要があります。このインデックスの作成の詳細については、「 Atlas Search とベクトル検索インデックスのガイド」を参照してください。

ベクトル検索集計ステージ

集計パイプラインで $vectorSearch ステージを作成するには、次のアクションを実行します。

パイプラインステージを保存するためのベクトルを作成します。
$vectorSearch 演算子を指定し、ベクトル検索クエリーの詳細を指定します。

$vectorSearch ステージでは、次のフィールドを定義する必要があります。

Parameter	タイプ	説明
`index`	string	ベクトル検索インデックスの名前
`path`	string	ベクトル埋め込みを含むフィールド
`queryVector`	数字の配列	クエリのベクトル表現
`limit`	数値	返される結果の数

Atlas Search クエリの例

このセクションでは、Atlas ベクトル検索クエリの実行方法を学習できます。このセクションの例では、sample_mflix.embedded_movies コレクションのサンプルデータを使用します。このサンプルデータをロードする方法については、Atlas ドキュメントの「 Atlas へのデータのロード」チュートリアルを参照してください。

基本的なベクトル検索クエリ

次のコードでは、時間移動 というタームを表すクエリベクトルを使用して、plot_embeddingベクトルフィールドに対して Atlas ベクトル検索クエリを実行します。

let query_vector = Vector::Float32(vec![-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 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-0.03129636, -0.0034038792, -0.006321252, -0.0016161345, 0.033642255, -0.000056075285, -0.005005019, 0.004571828, -0.0024075406, -0.00010215386, 0.0098634185, 0.1980148, -0.003825407, -0.025191706, 0.035161756, 0.005358236, 0.025111731, 0.023485601, 0.0023342315, -0.011882754, 0.018287312, -0.0068910643, 0.003912045, 0.009243623, -0.001355387, -0.028603915, -0.012802451, -0.030150073, -0.014795128, -0.028630573, -0.0013487226, 0.002667455, 0.00985009, -0.0033972147, -0.021486258, 0.009503538, -0.017847456, 0.013062365, -0.014341944, 0.005078328, 0.025165047, -0.015594865, -0.025924796, -0.0018177348, 0.010996379, -0.02993681, 0.007324255, 0.014475234, -0.028577257, 0.005494857, 0.00011725306, -0.013315615, 0.015941417, 0.009376912, 0.0025158382, 0.008743787, 0.023832154, -0.008084005, -0.014195326, -0.008823762, 0.0033455652, -0.032362677, -0.021552904, -0.0056081535, 0.023298996, -0.025444955, 0.0097301295, 0.009736794, 0.015274971, -0.0012937407, -0.018087378, -0.0039387033, 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-0.006684466, 0.01306903, 0.00015765642, -0.00530492, 0.0005277429, 0.015421589, 0.015528221, 0.032202728, -0.003485519, -0.0014286962, 0.033908837, 0.001367883, 0.010509873, 0.025271678, -0.020993087, 0.019846799, 0.006897729, -0.010216636, -0.00725761, 0.01818068, -0.028443968, -0.011242964, -0.014435247, -0.013688826, 0.006101324, -0.0022509254, 0.013848773, -0.0019077052, 0.017181009, 0.03422873, 0.005324913, -0.0035188415, 0.014128681, -0.004898387, 0.005038341, 0.0012320944, -0.005561502, -0.017847456, 0.0008538855, -0.0047884234, 0.011849431, 0.015421589, -0.013942076, 0.0029790192, -0.013702155, 0.0001199605, -0.024431955, 0.019926772, 0.022179363, -0.016487904, -0.03964028, 0.0050849924, 0.017487574, 0.022792496, 0.0012504216, 0.004048667, -0.00997005, 0.0076041627, -0.014328616, -0.020259995, 0.0005598157, -0.010469886, 0.0016852784, 0.01716768, -0.008990373, -0.001987679, 0.026417969, 0.023792166, 0.0046917885, -0.0071909656, -0.00032051947, -0.023259008, -0.009170313, 0.02071318, -0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136]);
let pipeline = vec![
    doc! {
        "$vectorSearch": doc! {
            "queryVector": &query_vector,
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 5
        }
    },
    doc! {
        "$project": doc! {
            "_id": 0,
            "title": 1,
            "plot": 1,
        }
    },
];
let mut results = my_coll.aggregate(pipeline).await?;
while let Some(result) = results.try_next().await? {
    println!("{}", result);
}

{ "plot": "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title": "Thrill Seekers" }
{ "plot": "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title": "About Time" }
{ "plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title": "The Time Machine" }
{ "plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title": "Crusade in Jeans" }
{ "plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title": "Timecop" }

Tip

クエリベクトル型

上記の例では、クエリベクトルとして機能するために、bson v2.14 で導入された bson::binary::Vector のインスタンスを作成します。 vec の数値をクエリベクトルとして使用することもできますが、ストレージ効率を向上させるために Vector 型を使用することをお勧めします。

ベクトル検索スコア

次のコードは、前の例と同じクエリを実行しますが、ドキュメントがクエリベクトルにどの程度一致するかを示す titleフィールドと vectorSearchScore メタフィールドのみを出力します。

let pipeline = vec![
    doc! {
        "$vectorSearch": doc! {
            "queryVector": &query_vector,
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 5
        }
    },
    doc! {
        "$project": doc! {
            "_id": 0,
            "title": 1,
            "score": doc! { "$meta": "vectorSearchScore" },
        }
    },
];
let mut results = my_coll.aggregate(pipeline).await?;
while let Some(result) = results.try_next().await? {
    println!("{}", result);
}

{ "title": "Thrill Seekers", "score": 0.9332504272460938 }
{ "title": "About Time", "score": 0.9312690496444702 }
{ "title": "The Time Machine", "score": 0.9295310378074646 }
{ "title": "Crusade in Jeans", "score": 0.9290415048599243 }
{ "title": "Timecop", "score": 0.9283164739608765 }

ベクトル検索オプション

$vectorSearch ステージを使用して、さまざまなタイプの Atlas ベクトル検索クエリを実行できます。必要なクエリに応じて、ステージ定義で次のオプションを指定できます。

任意のパラメーター	タイプ	説明	デフォルト値
`exact`	ブール値	完全最近傍（`true`）検索または近似近傍（`false`）検索を実行するかどうかを指定します	`false`
`filter`	ドキュメント	検索するドキュメントの事前フィルターを指定します	フィルタリングなし
`numCandidates`	数値	検索中に使用する最近傍の数を指定します	No limit

これらのオプションの詳細については、Atlas ドキュメントの $vectorSearch 演算子参照の「フィールド」セクションを参照してください。

詳細情報

このガイドで言及されている概念の詳細については、次のサーバーマニュアルエントリを参照してください。

aggregate()メソッドの動作の詳細については、「データの取得」ガイドの「集計操作」セクションを参照してください。

API ドキュメント

このガイドで言及されているメソッドとタイプの詳細については、次のAPIドキュメントを参照してください。

戻る

集計

Indexes