自動埋め込みは、 MongoDB Community Edition v8.2 以降でのみプレビュー機能として利用できます。機能および関連するドキュメントは、プレビュー期間中にいつでも変更される可能性があります。詳しくは、プレビュー機能を参照してください。
Overview
このガイドでは、MongoDB Vector Search 機能を Go ドライバーで使用する方法を学ぶことができます。これには、$vectorSearchパイプライン ステージを使用します。このパイプライン ステージでは、ドキュメントに対してセマンティック検索を実行できます。セマンティック検索では、指定された検索用語やフレーズと意味は似ているものの、必ずしも同一とは言えない情報が対象となります。
サンプル データ
このページの例では、Atlasサンプルデータセットの sample_mflixデータベースにある embedded_moviesコレクションの plot_embeddingフィールドをクエリします。
plot_embeddingフィールドには、OpenAI の text-embedding-ada-002 埋め込みモデルを使用して作成された、1536 次元を持つベクトル埋め込みが含まれています。
MongoDB Atlasクラスターを無料で作成して、サンプルデータセットをロードする方法については、Atlas を使い始める を参照してください。
MongoDB ベクトル検索クエリを実行する
この機能を使用するには、ベクトル検索インデックスとベクトル埋め込みのインデックスを作成する必要があります。プログラムによってベクトル検索インデックスを作成する方法については、MongoDB Search とMongoDB ベクトル検索インデックスを参照してください。ベクトル埋め込みの詳細については、Atlas ドキュメントの「ベクトル検索用のベクトル埋め込みのインデックス作成方法」を参照してください。
ベクトル埋め込みにベクトル検索インデックスを作成したら、集計パイプラインでこのインデックスを参照ベクトル検索クエリーを実行できます。
次のplot_embeddingフィールドでは、クエリベクトル用のBSONバイナリベクトルを作成する方法と、ベクトル検索インデックスを使用してベクトル検索クエリーを実行する方法を示します。
BSONバイナリ ベクトルの作成
この例では、1536 次元ベクトルを作成して、plot_embeddingフィールドでのベクトル検索クエリーのベクトルとして使用できます。クエリは、ベクトル埋め込みを使用して string "time move" を検索し、plot_embeddingフィールドで検索します。
次の例では、このベクトル埋め込みを、クエリベクトルとして使用できるBSONバイナリベクトルに変換する方法を示しています。
// Converts a float32 array to a BSON vector vectorFloat := bson.NewVector([]float32{-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, 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-0.0041286405, 0.004801752, 0.0011787785, -0.001257086, -0.011216307, -0.013395589, 0.00088137644, -0.0051616337, 0.03876057, -0.0033455652, 0.00075850025, -0.006951045, -0.0062112883, 0.018140694, -0.006351242, -0.008263946, 0.018154023, -0.012189319, 0.0075508473, -0.044358727, -0.0040153447, 0.0093302615, -0.010636497, 0.032789204, -0.005264933, -0.014235313, -0.018393943, 0.007297597, -0.016114693, 0.015021721, 0.020033404, 0.0137688, 0.0011046362, 0.010616505, -0.0039453674, 0.012109346, 0.021099718, -0.0072842683, -0.019153694, -0.003768759, 0.039320387, -0.006747778, -0.0016852784, 0.018154023, 0.0010963057, -0.015035049, -0.021033075, -0.04345236, 0.017287642, 0.016341286, -0.008610498, 0.00236922, 0.009290274, 0.028950468, -0.014475234, -0.0035654926, 0.015434918, -0.03372223, 0.004501851, -0.012929076, -0.008483873, -0.0044685286, -0.0102233, 0.01615468, 0.0022792495, 0.010876419, -0.0059647025, 0.01895376, -0.0069976957, -0.0042952523, 0.017207667, -0.00036133936, 0.0085905045, 0.008084005, 0.03129636, -0.016994404, -0.014915089, 0.020100048, -0.012009379, -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}) // Converts the BSON vector to a BSON binary vector queryVector := vectorFloat.Binary()
元のベクトルのスライスにアクセスする必要がある場合は、クエリベクトルをBSONベクトルに逆直列化することもできます。
次の例は、NewVectorFromBinary() メソッドを使用して、クエリベクトルをBSONバイナリベクトルからBSONベクトルに変換する方法を示しています。
// Converts the BSON binary vector back to a BSON vector backToVectorFloat, err := bson.NewVectorFromBinary(queryVector) if err != nil { log.Fatal(err) }
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BSONバイナリベクトルのインスタンスを作成していますが、 BSON double 値の配列を使用することもできます。ただし、ストレージ効率を向上させるために、 BSONバイナリベクトルを使用することをお勧めします。
ベクトル検索クエリの実行
次の例は、$vectorSearch メソッドと $project メソッドを使用して次の仕様で近似近傍（ANN）ベクトル検索を実行する集計パイプラインを構築する方法を示しています。
BSONバイナリ
queryVectorを使用してplot_embeddingフィールドをクエリしますnumCandidatesオプションを使用して、検索で使用される最近傍の数を 150 に設定しますplot_embeddingフィールドで作成されたvector_searchインデックスを使用指定された
plot、title、scoreフィールドを持つ 5 ドキュメントを返します
// Creates the aggregation pipeline vectorSearchStage := bson.D{ {"$vectorSearch", bson.D{ {"index", "vector_index"}, {"path", "plot_embedding"}, {"queryVector", queryVector}, {"numCandidates", 150}, {"limit", 5}, }}} projectStage := bson.D{ {"$project", bson.D{ {"_id", 0}, {"plot", 1}, {"title", 1}, {"score", bson.D{{"$meta", "vectorSearchScore"}}}, }}} // Runs the aggregation pipeline cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage}) if err != nil { log.Fatalf("failed to retrieve data from the server: %v", err) } // Displays the results type ProjectedMovieResult struct { Title string `bson:"title"` Plot string `bson:"plot"` Score float64 `bson:"score"` } var results []ProjectedMovieResult if err = cursor.All(ctx, &results); err != nil { log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err) } for _, result := range results { fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score) }
Title: Thrill Seekers Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters. Score: 0.92730712890625 Title: About Time 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. Score: 0.926605224609375 Title: The Time Machine 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. Score: 0.9239959716796875 Title: Timecop 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. Score: 0.923583984375 Title: Crusade in Jeans 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... Score: 0.9222412109375
自動埋め込みインデックスをクエリする
自動埋め込みMongoDB ベクトル検索インデックスをクエリすることで、テキスト検索のベクトル生成を自動化できます。Goドライバーを使用して自動埋め込みインデックスを作成する方法については、「MongoDB自動埋め込み検索インデックス モデル」セクションをインデックスガイド参照してください。
次の例では、plotフィールドでフレーズ "time travel" へのセマンティック類似性を検索する集計パイプラインを構築します。クエリは、auto_embedding_index という名前の plotフィールドに自動埋め込みMongoDB ベクトル検索インデックスを使用します。
// Creates the $vectorSearch stage that queries the // auto-embedding index named "auto_embedding_index" vectorSearchStage := bson.D{ {"$vectorSearch", bson.D{ {"index", "auto_embedding_index"}, {"path", "plot"}, {"query", bson.D{{"text", "time travel"}}}, {"model", "voyage-4"}, {"numCandidates", 150}, {"limit", 10}, }}, } // Projects only the title and plot fields projectStage := bson.D{ {"$project", bson.D{ {"_id", 0}, {"title", 1}, {"plot", 1}, }}, } // Runs the aggregation pipeline cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage}) if err != nil { log.Fatalf("failed to retrieve data from the server: %v", err) } defer cursor.Close(ctx) type ProjectedMovieResult struct { Title string `bson:"title"` Plot string `bson:"plot"` } var results []ProjectedMovieResult if err := cursor.All(ctx, &results); err != nil { log.Fatalf("failed to unmarshal retrieved docs: %v", err) } for _, result := range results { fmt.Printf("Title: %v\nPlot: %v\n---\n\n", result.Title, result.Plot) }
Title: Manuel on the Island of Wonders Plot: Manuel's fantasy travel through Time goes from Long Ago (Episode 1 - O jardim proibido / Le Jardin interdit) through Now (Episode 2 - O pique-nique dos sonhos / Le Pique-nique des rèves), ... --- Title: 11 Minutes Ago Plot: Traveling in 11-minute increments, a time-tumbler from 48-years in the future spends two years of his life weaving through a two-hour wedding reception. --- Title: Time Freak Plot: A neurotic inventor creates a time machine and gets lost traveling around yesterday. --- Title: Timecrimes Plot: A man accidentally gets into a time machine and travels back in time nearly an hour. Finding himself will be the first of a series of disasters of unforeseeable consequences. --- Title: The Little Girl Who Conquered Time Plot: A high-school girl acquires the ability to time travel. --- Title: The Little Girl Who Conquered Time Plot: A high-school girl acquires the ability to time travel. --- Title: Time Traveller Plot: A high-school girl acquires the ability to time travel. --- Title: Je t'aime je t'aime Plot: Recovering from an attempted suicide, a man is selected to participate in a time travel experiment that has only been tested on mice. A malfunction in the experiment causes the man to ... --- Title: A.P.E.X. 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: The Ah of Life Plot: Theoretical mathematician, Nigel Kline finds himself the subject of his own vertical time study. Entering into Einstein's relativity, three versions of Nigel face off with each other, weaving time and space in a world of fluid moments. --- Title: About Time 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. ---
注意
自動埋め込みMongoDB ベクトル検索インデックスをクエリする場合は、queryフィールドに検索するためのテキストを指定し（例: {"text", "time travel"}）、modelフィールドに互換性のあるモデル名を指定します。サポートされているモデルは、voyage-4-lite、voyage-4、voyage-4-large、voyage-code-3 です。queryVector 値を計算したり渡したりする必要はありません。
詳細情報
MongoDB Vector Search の詳細については、MongoDB Atlas ドキュメントの MongoDB Vector Search ガイドを参照してください。
$vectorSearch パイプライン ステージの構文の詳細については、MongoDB Atlas ドキュメントの MongoDB Vector Search セクションのガイドの「クエリの作成および実行」ガイドの構文とフィールドのセクションを参照してください。
API ドキュメント
このガイドで説明したメソッドや型の詳細については、次の API ドキュメントを参照してください。