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2. 演算子とコレクターの使用

knnBeta

項目一覧

  • 定義
  • 構文
  • オプション
  • 動作
  • 例

定義

knnBeta

knnBeta 演算子は Hierarchical Navigable Small Worlds アルゴリズムを使用してセマンティック検索を実行します。Atlas Search の kNN クエリ サポートを使用して、選択した製品に類似するものを検索したり、画像を検索したりできます。

構文

knnBeta の構文は次のとおりです。

1{
2  $search: {
3    "index": "<index name>", // optional, defaults to "default"
4    "knnBeta": {
5      "vector": [<array-of-numbers>],
6      "path": "<field-to-search>",
7      "filter": {<filter-specification>},
8      "k": <number>,
9      "score": {<options>}
10    }
11  }
12}

オプション

フィールド
タイプ
説明
必要性

filter

ドキュメント

メタデータまたは特定の検索条件に基づいてドキュメントをフィルタリングする任意の Atlas Search 演算子。これにより、ベクトル検索の範囲を絞り込むことができます。

任意

k

数値

返される最近傍の数。精度を高めるために、返されるドキュメントの数（$limit）よりも大きい数値を指定できます。

必須

path

string

検索するためのインデックスを作成した knnVector タイプ フィールド。詳細については、「パス構築」を参照してください。

必須

score

ドキュメント

結果で一致するドキュメントに割り当てられたスコア。詳細については、「 スコアリング動作 」を参照してください。

任意

vector

数字の配列

クエリ ベクトルを表す BSON 型int または double の数値の配列。配列のサイズは、フィールドのインデックスで指定されたベクトル dimensions の数と一致する必要があります。

必須

動作

Atlas Search タイプ knnVector としてインデックスが作成されたフィールドに対してのみ、kNN クエリを実行できます。

$search ステージの後に $limit を使用して、knnBeta クエリ結果内のドキュメントの数を制限できます。k の値を $limit の値よりも高く設定することをお勧めします。このオーバーリクエスト パターンは、近似最近傍探索でレイテンシと再現率のトレードオフを行う主な方法です。経験的に、5 ～ 10 倍の倍率が多くのユースケースでうまく機能することがわかっていますが、特定のデータセットに合わせてこれを調整することをお勧めします。

例

次のクエリは、クエリに対して最近傍の150 個を検索し、残りの結果数を 50 に制限します。

1db.<collection>.aggregate({
2  "$search": {
3    "knnBeta": {
4      "vector": <array-of-numbers-to-search>,
5      "path": <indexed-field-to-search>,
6      "k": 150
7    }
8  }
9},
10{
11  "$limit": 50
12})

パフォーマンス

クエリのパフォーマンスを向上させるには、結果内にすべてのフィールドが必要な場合を除き、$project ステージを使用して結果の中に返すフィールドを選択します。$project ステージではベクトル フィールドを除外することをお勧めします。

スコアリング

$project ステージで $meta 式 searchScore を含む score フィールドを使用すると、結果内にドキュメントのスコアを返すことができます。

Atlas Search は、kNN クエリの結果を 0 から 1 までの固定範囲でのみスコア付けします。cosine および dotProduct 類似性については、Atlas Search は以下のアルゴリズムを使用してスコアを正規化します。

score = (1 + cosine/dot_product(v1,v2)) / 2

制限

knnBeta 演算子はクエリの最上位演算子でなければならないため、以下の中で knnBeta 演算子を使用することができません。

knnBeta 演算子を使用して、 vectorSearch タイプのインデックスを使用してインデックス付けされたフィールドをクエリすることはできません。knnBeta 演算子では$search ソート オプションは使用できません。

$search ステージ以降に $skip と $limit を使用して Atlas Search の結果をページ分割することはお勧めしません。

例

以下のクエリは、knnBeta 演算子を使用してサンプル sample_mflix.embedded_movies コレクションを検索します。クエリは、OpenAI の text-embedding-ada-002 埋め込みモデルを使用して作成された埋め込みを含む plot_embedding フィールドを検索します。サンプル コレクションを Atlas クラスターに追加し、コレクションのサンプル インデックス定義を作成した場合は、sample_mflix データベースに切り替えて、コレクションに対して次のクエリを実行できます。

以下のクエリは、knnBeta 演算子を使用して plot_embedding フィールドで vector 次元を検索し、結果から最大 5 件の最近傍を要求します。また、以下を行うため、$project ステージの指定も行います。

  • _id フィールドを除外し、結果に plot フィールドと title フィールドを含める。

  • 結果に score という名前のフィールドを追加する。

1db.embedded_movies.aggregate([
2  {
3    "$search": {
4      "index": "default",
5      "knnBeta": {
6        "vector": 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1[
2  {
3    plot: 'When two girls move to the country to be near their ailing mother, they have adventures with the wonderous forest spirits who live nearby.',
4    title: 'My Neighbor Totoro',
5    score: 0.9999986886978149
6  },
7  {
8    plot: "To escape a war, a girl flees to a remote farmhouse and becomes part of an expansive family's unusual, perhaps even supernatural lifestyle.",
9    title: 'Black Moon',
10    score: 0.8282386660575867
11  },
12  {
13    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
14    title: 'Forest Warrior',
15    score: 0.8196926712989807
16  },
17  {
18    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
19    title: 'Forest Warrior',
20    score: 0.8196901082992554
21  },
22  {
23    plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.',
24    title: 'Coraline',
25    score: 0.815956711769104
26  }
27]

次のクエリは、1971 年 6 月 30 日（1971-06-30）当日、またはそれ以前（≤）にリリースされた映画のドキュメントをフィルタリングし、次にそのフィルタリングされたドキュメント内の plot_embedding フィールドで vector 次元を検索し、最大 5 個の最近傍を結果として返すことを要求します。クエリは、以下を行うため $project ステージも指定します。

  • _idフィールドを除外し、結果にはplot 、 title 、 releasedフィールドのみを含めます。

  • 結果に score という名前のフィールドを追加する。

1db.embedded_movies.aggregate([
2  {
3    "$search": {
4      "index": "default",
5      "knnBeta": {
6        "vector": 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7        "path": "plot_embedding",
8        "filter": {
9          "range": {
10            "lte": ISODate("1971-06-30 00:27:59.177000000"),
11            "path": "released"
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14        "k": 5
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17  },
18  {
19    "$project": {
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24      "score": { $meta: "searchScore" }
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27])
1[
2  {
3    plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.',
4    title: 'Willy Wonka & the Chocolate Factory',
5    released: ISODate("1971-06-30T00:00:00.000Z"),
6    score: 0.9999985694885254
7  },
8  {
9    plot: 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.',
10    title: 'Chitty Chitty Bang Bang',
11    released: ISODate("1968-12-18T00:00:00.000Z"),
12    score: 0.7576897740364075
13  },
14  {
15    plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.',
16    title: 'The Red Balloon',
17    released: ISODate("1957-03-11T00:00:00.000Z"),
18    score: 0.7515948414802551
19  },
20  {
21    plot: 'While hearing the story of "Gulliver's Travels", a young boy dreams he is the title character on the island of Lilliput .',
22    title: 'The New Gulliver',
23    released: ISODate("1935-11-03T00:00:00.000Z"),
24    score: 0.7495509386062622
25  },
26  {
27    plot: 'A living puppet, with the help of a cricket as his conscience, must prove himself worthy to become a real boy.',
28    title: 'Pinocchio',
29    released: ISODate("1940-02-23T00:00:00.000Z"),
30    score: 0.7472590804100037
31  }
32]

Atlas Search は、1971-06-30 以下の released フィールド値に基づいてドキュメントをフィルタリングします。タイトルが Willy Wonka & the Chocolate Factory のドキュメントは、filter オプション内の検索条件とクエリ内の vector 次元に一致するため、スコアが高くなります。

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