MongoDB ベクトル検索クエリを実行する

Overview

このガイドでは、 MongoDB Vector Search 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。C++ドライバーを使用すると、集計フレームワークを使用してMongoDB 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

MongoDB ベクトル検索機能はMongoDB Atlasクラスターでのみ使用できます。この機能は、自己管理型の配置では使用できません。

MongoDB ベクトル検索の詳細については、Atlas ドキュメントのMongoDB ベクトル検索概要を参照してください。

注意

MongoDB 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、 MongoDB Search 機能を使用できます。この機能については、 MongoDB Search の概要。を参照してください。

MongoDB ベクトル検索インデックス

MongoDB ベクトル検索クエリを実行する前に、コレクションにMongoDB 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、 MongoDB 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	データ型	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`	`BSON::Binary` ベクトルサブタイプ、`BSON::Vector`、または数値の配列	クエリのベクトル表現
`limit`	`number`	返される結果の数

次に、ステージを集計パイプラインに含め、そのパイプラインを aggregate メソッドに渡します。

MongoDB 検索クエリの例

このセクションでは、 MongoDB 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、「タイムトラベル」というフレーズのベクトル埋め込みである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MongoDB ベクトル検索クエリを実行します。

#include <bsoncxx/builder/basic/document.hpp>
#include <bsoncxx/builder/list.hpp>
#include <bsoncxx/json.hpp>
#include <mongocxx/client.hpp>
#include <mongocxx/instance.hpp>
#include <mongocxx/uri.hpp>
using bsoncxx::builder::basic::kvp;
using bsoncxx::builder::basic::make_document;
int main() {
  try {
    mongocxx::instance inst{};
    // Replace the placeholder with your Atlas connection string
    const auto uri = mongocxx::uri{"<connection-string>"};
    // Connect to your Atlas cluster
    auto client = mongocxx::client{uri};
    auto db = client["sample_mflix"];
    auto collection = db["embedded_movies"];
    // Define vector embeddings to search
    auto vectors = bsoncxx::builder::list{
      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024262359,0.054507218,-0.009721561,0.005816319,-0.00206687,-0.008766023,0.017365867,-0.000737426,0.018362951,-0.023597637,-0.019110762,0.021935832,0.041545134,-0.020357117,-0.017615138,0.044868745,-0.018030589,-0.032405205,-0.050186522,-0.014540797,0.005213914,-0.006688767,0.047527634,0.040714234
    };
    // Define the pipeline with vectorSearch query options
    mongocxx::pipeline stages;
    stages
        .append_stage(make_document(
            kvp("$vectorSearch",
                make_document(kvp("index", "vector_index"),
                              kvp("path", "plot_embedding_voyage_3_large"),
                              kvp("queryVector", vectors),
                              kvp("numCandidates", 150), kvp("limit", 10)))))
        .project(make_document(
            kvp("_id", 0), kvp("plot", 1), kvp("title", 1)));
    auto cursor = collection.aggregate(stages);
    for (auto&& doc : cursor) {
      std::cout << bsoncxx::to_json(doc) << std::endl;
    }
  } catch (const std::exception& e) {
    std::cout << "Exception: " << e.what() << std::endl;
  }
  return 0;
}

{ "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" : "A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.", "title" : "Retroactive" }
{ "plot" : "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title" : "A.P.E.X." }
{ "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" }
{ "plot" : "After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.", "title" : "Back to the Future Part II" }
{ "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" : "Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...", "title" : "Timerider: The Adventure of Lyle Swann" }
{ "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" : "A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.", "title" : "The Time Traveler's Wife" }
{ "plot" : "A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.", "title" : "The Final Countdown" }

このクエリは、 $vectorSearchステージを使用して次の操作を実行します。

近似最近傍探索（ANN）ベクトル検索を実行する
plot_embedding_voyage_3_large フィールドで指定した期間を検索する
numCandidates オプションを使用して、検索で使用する最近傍の数を 150 に設定する
limit オプションを使用してクエリから最大 10 件のドキュメントを返す

$projectステージは次の目的で使用されます。

結果に「movie」のplot フィールドと title フィールドのみを含める

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、$project ステージを使用して titleフィールドと vectorSearchScore メタフィールドのみを出力します。これは、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します。

// Define the pipeline with vectorSearch and project stages
mongocxx::pipeline score_pipeline;
score_pipeline
    .append_stage(make_document(
        kvp("$vectorSearch",
            make_document(kvp("queryVector", vectors),
                          kvp("index", "vector_index"),
                          kvp("path", "plot_embedding_voyage_3_large"),
                          kvp("numCandidates", 150),
                          kvp("limit", 10)))))
    .project(make_document(
        kvp("_id", 0),
        kvp("title", 1),
        kvp("score", make_document(kvp("$meta", "vectorSearchScore")))));
auto cursor = collection.aggregate(score_pipeline);
for (auto&& doc : cursor) {
  std::cout << bsoncxx::to_json(doc) << std::endl;
}

{ "title" : "About Time", "score" : 0.77101051807403564453 }
{ "title" : "Retroactive", "score" : 0.76004779338836669922 }
{ "title" : "A.P.E.X.", "score" : 0.7576859593391418457 }
{ "title" : "Timecop", "score" : 0.757656097412109375 }
{ "title" : "Back to the Future Part II", "score" : 0.75213932991027832031 }
{ "title" : "Thrill Seekers", "score" : 0.75099325180053710938 }
{ "title" : "Timerider: The Adventure of Lyle Swann", "score" : 0.75026416778564453125 }
{ "title" : "The Time Machine", "score" : 0.75025022029876708984 }
{ "title" : "The Time Traveler's Wife", "score" : 0.74949628114700317383 }
{ "title" : "The Final Countdown", "score" : 0.7469132542610168457 }

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	データ型	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、集計ガイド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。

Aggregate()

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