ベクトル検索クエリの実行
項目一覧
Atlas Vector Search クエリは集計パイプライン ステージの形式をとります。 $vectorSearchクエリの場合、Atlas Vector Search はセマンティック検索の結果を返します。
注意
定義
$vectorSearchステージは、指定されたフィールド内のベクトルに対してANNまたはENN検索を実行します。
ANN 検索
ANN検索では、Atlas Vector Search は、多次元空間での近接性と考慮事項の数に基づいて、クエリ内のベクトル埋め込みに最も近いデータ内のベクトル埋め込みを検索します。 Hierarchical Navigable Small Worlds を使用する アルゴリズムを使用し、すべてのベクトルをスキャンせずにクエリ内のベクトル埋め込みに最も類似するベクトル埋め込みを検索します。したがって、 Ann検索は、重要なフィルタリングなしで大規模なデータセットをクエリするのに最適です。
ENN 検索
EXN検索では、Atlas Vector Search は、すべての埋め込み間の距離を計算して、すべてのインデックス作成されたベクトル埋め込みを網羅的に検索し、クエリに埋め込まれたベクトル埋め込みの完全最近傍を見つけます。 計算負荷が高く、クエリのレイテンシに悪影響を与える可能性があります。 したがって、次のユースケースにはUNIX検索が推奨されます。
ENN クエリの理想的かつ正確な結果を使用して、 ANN クエリの再現率と精度を判断します。
考慮する最近傍の数を調整する必要なしに、 10000未満のドキュメントをクエリしたい場合
データの5 % 未満が指定されたプレフィルターを満たすコレクションに対して、クエリに選択的なプレフィルターを含めたい。
構文
検索するフィールドは、 vectorSearch インデックス タイプ内で Atlas Vector Search ベクトル タイプとしてインデックス付けする必要があります。
フィールド
$vectorSearchステージは、次のフィールドを持つドキュメントを取得します。
フィールド | タイプ | 必要性 | 説明 |
|---|---|---|---|
| ブール値 | 任意 |
EXN検索またはANN検索のどちらを実行するかを指定するフラグ。 値は次のいずれかになります。
省略した場合、デフォルトは Atlas Vector Search は MongoDB v 6.0.11を実行しているクラスターでのAnn検索をサポートします v 7.0.2以降、MongoDB v 6を実行しているクラスターでの とENN検索。 0 。 16 、 v 7 . 0 。 10 、 v 7 . 3 。 2 、 以降に更新します。 |
| ドキュメント | 任意 | インデックス付きフィールドをブール値、date、objectId、数値（10 進数ではない）、string、または UUID と比較する任意のMQLマッチ式で、プレフィルターとして使用します。 Atlas ベクトル検索がフィルターでサポートするクエリと集計パイプライン演算子については、「 Atlas ベクトル検索プレフィルター 」を参照してください。 |
| string | 必須 | 使用する Atlas Vector Search インデックスの名前。 Atlas Vector Search では、インデックス名のスペルをうっかり間違えた場合、または指定されたインデックスがクラスター上にまだ存在しない場合には結果が返されません。 |
| 数値 | 必須 | 結果で返されるドキュメントの数（ |
| 数値 | 任意 | このフィールドは、 検索中に使用する最近傍の数。 値は （ 精度を高めるために、返されるドキュメント数（ |
| string | 必須 | |
| 数字の配列 | 必須 | クエリベクトルを表すBSON クエリ用にBSON BinData 配列のサイズは、フィールドのインデックス定義で指定されたベクトル データの埋め込みに使用したのと同じモデルを使用してクエリを埋め込む必要があります。 |
動作
$vectorSearch は、それが表示されるすべてのパイプラインの最初のステージである必要があります。
Atlas Vector Search インデックス
$vectorSearchvectorSearch タイプのインデックス定義内で ステージを使用して検索するフィールドをインデックスする必要があります。Atlas Vector Search vectorSearchタイプのインデックス定義では、次のタイプのフィールドにインデックスを付けることができます。
ベクトル型としてのベクトル埋め込みを含むフィールド。
事前にフィルタリングされたデータのベクトル検索を有効にするフィルタータイプとしてブール値、日付、ObjectId、数値、string、および UUID の値を含むフィールド。
これらの Atlas Vector Search フィールド タイプの詳細については、 「ベクトル検索のフィールドにインデックスを作成する方法」 を参照してください。
Atlas Vector Search Score
Atlas Vector Search は、返されるすべてのドキュメントに、 0から1までの固定範囲のスコアを割り当てます（ 0は類似性が低く、 1は類似性が高いことを示します）。 cosineおよびdotProduct類似性の場合、Atlas Vector Search はスコアを正規化し、スコアが負でないことを確認します。
cosineの場合、Atlas Vector Search は次のアルゴリズムを使用してスコアを正規化します。
score = (1 + cosine(v1,v2)) / 2
dotProductの場合、Atlas Vector Search は次のアルゴリズムを使用してスコアを正規化します。
score = (1 + dotProduct(v1,v2)) / 2
これらのアルゴリズムでは、Atlas Vector Search が、ドキュメント ベクトル（ v1 ）とクエリ ベクトル（ v2 ）の類似性スコアを考慮してスコアを正規化していることを示しています。クエリ ベクトルは [ -1 、 1 ] です。 Atlas Vector Search は類似性スコアに1を追加して、スコアを範囲 [ 0 、 2 ] に正規化し、 2で割って0と1の間の値を確保します。
euclidean類似性の場合、Atlas Vector Search は次のアルゴリズムを使用してスコアを正規化し、 0と1の間の値を確保します。
score = 1 / (1 + euclidean(v1,v2))
上記のアルゴリズムは、Atlas Vector Search が、ドキュメントベクトル（ v1 ）とクエリベクトル（ v2 ）の距離である ユークリッド距離 を計算してスコアを正規化することを示しています。この距離は [ 0 、 ∞ ] 次に、Atlas Vector Search は、距離に1を追加して距離を類似性スコアに変換し、 1を類似性スコアで割って0と1の間の値を確保します。
返されたドキュメントに割り当てられるスコアは、ドキュメントのメタデータの一部です。 返された各ドキュメントのスコアを結果セットに含めるには、集計パイプラインで$projectステージを使用します。
Atlas Vector Search クエリ結果のスコアを検索するには、 $meta式の値としてvectorSearchScore を使用します。 つまり、 $vectorSearch ステージの後の$project ステージでは、 score フィールドは$meta式を取ります。 式では、ベクトル検索のドキュメントのスコアを返すためにvectorSearchScore 値が必要です。
例
1 db.<collection>.aggregate([ 2 { 3 "$vectorSearch": { 4 <query-syntax> 5 } 6 }, 7 { 8 "$project": { 9 "<field-to-include>": 1, 10 "<field-to-exclude>": 0, 11 "score": { "$meta": "vectorSearchScore" } 12 } 13 } 14 ])
注意
データを事前にフィルタリングしても、Atlas Vector Search が$vectorSearchクエリに$vectorSearchScoreを使用して返すスコアには影響しません。
Atlas Vector Search 事前フィルター
$vectorSearch filter オプションは、 BSONブール値、日付、 ObjectId 、数値、 string 、UUID の値のみと一致します。 vectorSearch タイプのインデックス定義の フィルター タイプとして、データをフィルタリングするフィールドをインデックスする 必要 があります。データをフィルタリングすると、セマンティック検索の範囲を絞り込み、すべてのベクトルが比較の対象とならないようにするのに役立ちます。
Atlas Vector Search は、次の$vectorSearchfilter MQL 一致式に対して オプションをサポートしています。
$vectorSearch filterオプションは、次の集計パイプライン演算子のみをサポートします。
注意
$vectorSearch filter オプションは、他のクエリ演算子、集計パイプライン演算子、または Atlas Search 演算子をサポートしていません。
Considerations
Atlas Vector Search は $eq の短縮形式をサポートしています。短縮形式では、クエリに $eq を指定する必要はありません。
例
$eq の付いた次のフィルターを例に挙げます。
"filter": { "_id": { "$eq": ObjectId("5a9427648b0beebeb69537a5") }
次の方法で、$eq の短縮形を使用して、上記のクエリを実行できます。
"filter": { "_id": ObjectId("5a9427648b0beebeb69537a5") }
$and 演算子を使用して、単一のクエリでフィルターの配列を指定することもできます。
例
たとえば、genres フィールドが Action に等しく、かつ year フィールドの値が 1999、 2000、または 2001 に等しいドキュメントの次のプレフィルターを考えてみましょう。
"filter": { "$and": [ { "genres": "Action" }, { "year": { "$in": [ 1999, 2000, 2001 ] } } ] }
制限
$vectorSearch は、次の MongoDB バージョンを実行している Atlas クラスターでのみサポートされています。
v6.0.11
v7.0.2 以降（ RCを含む）。
$vectorSearchは、ビュー定義と次のパイプライン ステージでは使用できません。
$lookupサブパイプライン$unionWithサブパイプライン$facetパイプライン ステージ
このステージには$vectorSearchの結果を渡すことができます。
サポートされているクライアント
$vectorSearchAtlas UI、mongosh 、任意のMongoDBドライバー を使用して、 クエリを実行できます。
Atlas Vector Search は、Atlas CLI を使用して作成したローカル Atlas 配置で使用することもできます。 詳細については、「 Atlas 配置のローカル配置の作成 」を参照してください。
セグメント間でのクエリの並列実行
ベクトル検索クエリ処理を分離するには、専用の検索ノードをお勧めします。 専用の検索ノードではクエリのパフォーマンスが向上する場合があります。 高 CPU システムではパフォーマンスがさらに向上する可能性があることに注意してください。 Atlas Vector Search が検索ノードで実行される場合、Atlas Vector Search はデータのセグメント全体でクエリの実行を並列化します。
クエリ処理を並列化すると、大規模なデータセットに対するクエリなど、多くのケースで応答時間が向上します。 Atlas Vector Search クエリ処理中にクエリ内並列処理を使用すると、より多くのリソースが使用されますが、個々のクエリのレイテンシが向上します。
注意
Atlas Vector Search では、各クエリが同時に実行されることは保証されません。 例えば、同時クエリがキューに入れられすぎると、Atlas Vector Search はシングルスレッド実行に戻る可能性があります。
同じ連続するクエリに対して一貫性のない結果が表示される場合があります。 これを軽減するには、 $vectorSearchクエリでnumCandidatesの値を増やします。
例
次のクエリは、 $vectorSearchステージ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 インデックスを作成した場合は、コレクションに対して次のクエリを実行できます。
注意
サンプル コードからqueryVectorをターミナルに貼り付けると、マシンによっては時間がかかる場合があります。
➤ [言語の選択] ドロップダウン メニューを使用して、このページの例の言語を設定します。
ANN の例
次のクエリは、 $vectorSearchステージで string時間移動のベクトル埋め込みを使用してplot_embeddingフィールドを検索します。 最大150の最近傍を考慮し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、 フィールドとplottitleフィールドのみを結果に含めます。結果内の各ドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding", 6 "queryVector": 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7 "numCandidates": 150, 8 "limit": 10, 9 "quantization": "scalar" 10 } 11 }, 12 { 13 "$project": { 14 "_id": 0, 15 "plot": 1, 16 "title": 1, 17 "score": { $meta: "vectorSearchScore" } 18 } 19 } 20 ])
1 [ 2 { 3 plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', 4 title: 'Thrill Seekers', 5 score: 0.7892671227455139 6 }, 7 { 8 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.', 9 title: 'About Time', 10 score: 0.7843604683876038 11 }, 12 { 13 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.', 14 title: 'The Time Machine', 15 score: 0.7801066637039185 16 }, 17 { 18 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...", 19 title: 'Crusade in Jeans', 20 score: 0.7789170742034912 21 }, 22 { 23 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.', 24 title: 'Timecop', 25 score: 0.7771612405776978 26 }, 27 { 28 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...', 29 title: 'A.P.E.X.', 30 score: 0.7730885744094849 31 }, 32 { 33 plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 34 title: 'Men in Black 3', 35 score: 0.7712380886077881 36 }, 37 { 38 plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 39 title: 'Tomorrowland', 40 score: 0.7669923901557922 41 }, 42 { 43 plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 44 title: 'Love Story 2050', 45 score: 0.7649372816085815 46 }, 47 { 48 plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 49 title: 'The Portal', 50 score: 0.7640786170959473 51 } 52 ]
次のクエリは、サンプル ベクトル データに対してセマンティック検索を実行する前に、 01年 1 月1955から 1 月01日1975の間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ます。 $and演算子を使用して、指定された日付の論理AND演算を実行します。 次に、string charactersのベクトル埋め込みを使用して、フィルタリングされたドキュメント内のplot_embeddingフィールドで150の最近傍を検索し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、結果にはplot、title、yearフィールドのみを含めます。結果内にドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding", 6 "filter": { 7 "$and": [ 8 { 9 "year": { "$gt": 1955 } 10 }, 11 { 12 "year": { "$lt": 1975 } 13 } 14 ] 15 }, 16 "queryVector": 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17 "numCandidates": 150, 18 "limit": 10 19 } 20 }, 21 { 22 "$project": { 23 "_id": 0, 24 "title": 1, 25 "plot": 1, 26 "year": 1, 27 "score": { $meta: "vectorSearchScore" } 28 } 29 } 30 ])
1 [ 2 { 3 plot: 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...', 4 title: 'Peter Pan', 5 year: 1960, 6 score: 0.748110830783844 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 year: 1968, 12 score: 0.7442465424537659 13 }, 14 { 15 plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 16 title: 'That Man from Rio', 17 year: 1964, 18 score: 0.7416020035743713 19 }, 20 { 21 plot: 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.', 22 title: 'The Little Prince', 23 year: 1974, 24 score: 0.7378944158554077 25 }, 26 { 27 plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.', 28 title: 'The Red Balloon', 29 year: 1956, 30 score: 0.7342712879180908 31 }, 32 { 33 plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.', 34 title: 'Willy Wonka & the Chocolate Factory', 35 year: 1971, 36 score: 0.7342107892036438 37 }, 38 { 39 plot: 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.', 40 title: 'Bedknobs and Broomsticks', 41 year: 1971, 42 score: 0.7339356541633606 43 }, 44 { 45 plot: "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", 46 title: 'Pastoral Hide and Seek', 47 year: 1974, 48 score: 0.733299970626831 49 }, 50 { 51 plot: 'A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.', 52 title: 'The Three Musketeers', 53 year: 1973, 54 score: 0.7331198453903198 55 }, 56 { 57 plot: 'A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.', 58 title: 'Frosty', 59 year: 1964, 60 score: 0.7318308353424072 61 } 62 ]
Atlas Vector Search は、 1955から1975の範囲のyear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。 プロットには、 1955から1975の間に公開された映画のプロットに、リクエストの結果をまとめたドキュメントが返されます。
Tip
「 その他のフィルターの例 」も参照してください
「 Atlas クラスターのデータに対してセマンティック検索を実行する方法 」では、 sample_mflix.embedded_moviesコレクション内の埋め込みデータに対するセマンティック検索クエリの他の事前フィルターについて説明します。
次のクエリは、 $vectorSearchステージで string時間移動のベクトル埋め込みを使用してplot_embeddingフィールドを検索します。 最大150の最近傍を考慮し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、 フィールドとplottitleフィールドのみを結果に含めます。結果内の各ドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 using MongoDB.Bson; 2 using MongoDB.Bson.Serialization.Attributes; 3 using MongoDB.Bson.Serialization.Conventions; 4 using MongoDB.Driver; 5 6 public class vectorSearchBasicQuery 7 { 8 // define connection to your Atlas cluster 9 private const string MongoConnectionString = "<connection-string>"; 10 11 public static void Main(string[] args){ 12 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() }; 13 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true); 14 15 // connect to your Atlas cluster 16 var mongoClient = new MongoClient(MongoConnectionString); 17 18 // define namespace 19 var moviesDatabase = mongoClient.GetDatabase("sample_mflix"); 20 var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies"); 21 22 // define vector embeddings to search 23 var vector = new[] 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24 var options = new VectorSearchOptions<EmbeddedMovie>() { 25 IndexName = "vector_index", 26 NumberOfCandidates = 150 27 }; 28 29 // run query 30 var results = moviesCollection.Aggregate() 31 .VectorSearch(movie => movie.Embedding, vector, 10, options) 32 .Project(Builders<EmbeddedMovie>.Projection 33 .Include(movie => movie.Title) 34 .Include(movie => movie.Plot) 35 .Exclude(movie => movie.Id) 36 .MetaVectorSearchScore(movie => movie.Score)) 37 .ToList(); 38 39 // print results 40 foreach (var movie in results) 41 { 42 Console.WriteLine(movie.ToJson()); 43 } 44 } 45 } 46 47 [] 48 public class EmbeddedMovie 49 { 50 [] 51 public ObjectId Id { get; set; } 52 public string? Title { get; set; } 53 public string? Plot { get; set; } 54 [] 55 public double[]? Embedding { get; set; } 56 public double Score { get; set; } 57 }
1 { "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", "score" : 0.78926712274551392 } 2 { "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", "score" : 0.78436046838760376 } 3 { "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", "score" : 0.78010666370391846 } 4 { "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", "score" : 0.77891707420349121 } 5 { "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", "score" : 0.77716124057769775 } 6 { "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.", "score" : 0.77308857440948486 } 7 { "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : 0.77123808860778809 } 8 { "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland", "score" : 0.76699239015579224 } 9 { "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : 0.76493728160858154 } 10 { "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal", "score" : 0.76407861709594727 }
次のクエリは、サンプル ベクトル データに対してセマンティック検索を実行する前に、 01年 1 月1955から 1 月01日1975の間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ます。 $and演算子を使用して、指定された日付の論理AND演算を実行します。 次に、string charactersのベクトル埋め込みを使用して、フィルタリングされたドキュメント内のplot_embeddingフィールドで150の最近傍を検索し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、結果にはplot、title、yearフィールドのみを含めます。結果内にドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 using System.Reflection.Emit; 2 using MongoDB.Bson; 3 using MongoDB.Bson.Serialization.Attributes; 4 using MongoDB.Bson.Serialization.Conventions; 5 using MongoDB.Driver; 6 using MongoDB.Driver.Search; 7 8 public class vectorSearchFilterQuery 9 { 10 // define connection to your Atlas cluster 11 private const string MongoConnectionString = "<connection-string>"; 12 13 public static void Main(string[] args){ 14 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() }; 15 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true); 16 17 // connect to your Atlas cluster 18 var mongoClient = new MongoClient(MongoConnectionString); 19 20 // define namespace 21 var moviesDatabase = mongoClient.GetDatabase("sample_mflix"); 22 var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies"); 23 24 // define vector embeddings to search 25 var vector = new[] 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26 // define filter 27 var yearGtFilter = Builders<EmbeddedMovie>.Filter.Gt("year", 1955); 28 var yearLtFilter = Builders<EmbeddedMovie>.Filter.Lt("year", 1975); 29 // define options 30 var options = new VectorSearchOptions<EmbeddedMovie>() { 31 Filter = Builders<EmbeddedMovie>.Filter.And(yearGtFilter, yearLtFilter), 32 IndexName = "vector_index", 33 NumberOfCandidates = 150 34 }; 35 36 // run query 37 var results = moviesCollection.Aggregate() 38 .VectorSearch(m => m.Embedding, vector, 10, options) 39 .Project(Builders<EmbeddedMovie>.Projection 40 .Include(m => m.Title) 41 .Include(movie => movie.Plot) 42 .Include(movie => movie.Year) 43 .MetaVectorSearchScore(m => m.Score)) 44 .ToList(); 45 46 // print results 47 foreach (var movie in results) 48 { 49 Console.WriteLine(movie.ToJson()); 50 } 51 } 52 } 53 54 [] 55 public class EmbeddedMovie 56 { 57 [] 58 public string Title { get; set; } 59 public string Plot { get; set; } 60 public int Year { get; set; } 61 [] 62 public double[] Embedding { get; set; } 63 public double Score { get; set; } 64 }
1 { "_id" : ObjectId("573a1395f29313caabce143b"), "plot" : "In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...", "title" : "Peter Pan", "year" : 1960, "score" : 0.74811083078384399 } 2 { "_id" : ObjectId("573a1396f29313caabce34d3"), "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.", "title" : "Chitty Chitty Bang Bang", "year" : 1968, "score" : 0.74424654245376587 } 3 { "_id" : ObjectId("573a1395f29313caabce23cb"), "plot" : "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "title" : "That Man from Rio", "year" : 1964, "score" : 0.74160200357437134 } 4 { "_id" : ObjectId("573a1396f29313caabce5648"), "plot" : "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title" : "The Little Prince", "year" : 1974, "score" : 0.73789441585540771 } 5 { "_id" : ObjectId("573a1394f29313caabce000d"), "plot" : "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title" : "The Red Balloon", "year" : 1956, "score" : 0.73427128791809082 } 6 { "_id" : ObjectId("573a1396f29313caabce482f"), "plot" : "A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.", "title" : "Willy Wonka & the Chocolate Factory", "year" : 1971, "score" : 0.7342107892036438 } 7 { "_id" : ObjectId("573a1396f29313caabce43e1"), "plot" : "An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.", "title" : "Bedknobs and Broomsticks", "year" : 1971, "score" : 0.7339356541633606 } 8 { "_id" : ObjectId("573a1396f29313caabce54e6"), "plot" : "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", "title" : "Pastoral Hide and Seek", "year" : 1974, "score" : 0.73329997062683105 } 9 { "_id" : ObjectId("573a1396f29313caabce582d"), "plot" : "A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.", "title" : "The Three Musketeers", "year" : 1973, "score" : 0.73311984539031982 } 10 { "_id" : ObjectId("573a1395f29313caabce243e"), "plot" : "A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.", "title" : "Frosty", "year" : 1964, "score" : 0.73183083534240723 }
Atlas Vector Search は、 1955から1975の範囲のyear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。 プロットには、 1955から1975の間に公開された映画のプロットに、リクエストの結果をまとめたドキュメントが返されます。
Tip
「 その他のフィルターの例 」も参照してください
「 Atlas クラスターのデータに対してセマンティック検索を実行する方法 」では、 sample_mflix.embedded_moviesコレクション内の埋め込みデータに対するセマンティック検索クエリの他の事前フィルターについて説明します。
次のクエリは、 $vectorSearchステージで string時間移動のベクトル埋め込みを使用してplot_embeddingフィールドを検索します。 最大150の最近傍を考慮し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、 フィールドとplottitleフィールドのみを結果に含めます。結果内の各ドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/bson" 9 "go.mongodb.org/mongo-driver/mongo" 10 "go.mongodb.org/mongo-driver/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(ctx, clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [1536]float64{ 31 -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, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, -0.020473259, 0.004671795, -0.012555866, 0.0209531, 0.01982014, 0.024485271, 0.0105431955, -0.005178295, 0.033162415, -0.013795458, 0.007150979, 0.010243294, 0.005644808, 0.017260984, -0.0045618312, 0.0024725192, 0.004305249, -0.008197301, 0.0014203656, 0.0018460588, 0.005015015, -0.011142998, 0.01439526, 0.022965772, 0.02552493, 0.007757446, -0.0019726837, 0.009503538, -0.032042783, 0.008403899, -0.04609149, 0.013808787, 0.011749465, 0.036388017, 0.016314628, 0.021939443, -0.0250051, -0.017354285, -0.012962398, 0.00006107364, 0.019113706, 0.03081652, -0.018114036, -0.0084572155, 0.009643491, -0.0034721901, 0.0072642746, -0.0090636825, 0.01642126, 0.013428912, 0.027724205, 0.0071243206, -0.6858542, -0.031029783, -0.014595194, -0.011449563, 0.017514233, 0.01743426, 0.009950057, 0.0029706885, -0.015714826, -0.001806072, 0.011856096, 0.026444625, -0.0010663156, -0.006474535, 0.0016161345, -0.020313311, 0.0148351155, -0.0018393943, 0.0057347785, 0.018300641, -0.018647194, 0.03345565, -0.008070676, 0.0071443142, 0.014301958, 0.0044818576, 0.003838736, -0.007350913, -0.024525259, -0.001142124, -0.018620536, 0.017247654, 0.007037683, 0.010236629, 0.06046009, 0.0138887605, -0.012122675, 0.037694257, 0.0055081863, 0.042492677, 0.00021784494, -0.011656162, 0.010276617, 0.022325981, 0.005984696, -0.009496873, 0.013382261, -0.0010563189, 0.0026507939, -0.041639622, 0.008637156, 0.026471283, -0.008403899, 0.024858482, -0.00066686375, -0.0016252982, 0.027590916, 0.0051449724, 0.0058647357, -0.008743787, -0.014968405, 0.027724205, -0.011596181, 0.0047650975, -0.015381602, 0.0043718936, 0.002159289, 0.035908177, -0.008243952, -0.030443309, 0.027564257, 0.042625964, -0.0033688906, 0.01843393, 0.019087048, 0.024578573, 0.03268257, -0.015608194, -0.014128681, -0.0033538956, -0.0028757197, -0.004121976, -0.032389335, 0.0034322033, 0.058807302, 0.010943064, -0.030523283, 0.008903735, 0.017500903, 0.00871713, -0.0029406983, 0.013995391, -0.03132302, -0.019660193, -0.00770413, -0.0038853872, 0.0015894766, -0.0015294964, -0.006251275, -0.021099718, -0.010256623, -0.008863748, 0.028550599, 0.02020668, -0.0012962399, -0.003415542, -0.0022509254, 0.0119360695, 0.027590916, -0.046971202, -0.0015194997, -0.022405956, 0.0016677842, -0.00018535563, -0.015421589, -0.031802863, 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-0.017620865, 0.04102649, 0.00552818, 0.024485271, -0.009630162, -0.015608194, 0.0006718621, -0.0008418062, 0.012395918, 0.0057980907, 0.016221326, 0.010616505, 0.004838407, -0.012402583, 0.019900113, -0.0034521967, 0.000247002, -0.03153628, 0.0011038032, -0.020819811, 0.016234655, -0.00330058, -0.0032289368, 0.00078973995, -0.021952773, -0.022459272, 0.03118973, 0.03673457, -0.021472929, 0.0072109587, -0.015075036, 0.004855068, -0.0008151483, 0.0069643734, 0.010023367, -0.010276617, -0.023019087, 0.0068244194, -0.0012520878, -0.0015086699, 0.022046074, -0.034148756, -0.0022192693, 0.002427534, -0.0027124402, 0.0060346797, 0.015461575, 0.0137554705, 0.009230294, -0.009583511, 0.032629255, 0.015994733, -0.019167023, -0.009203636, 0.03393549, -0.017274313, -0.012042701, -0.0009930064, 0.026777849, -0.013582194, -0.0027590916, -0.017594207, -0.026804507, -0.0014236979, -0.022032745, 0.0091236625, -0.0042419364, -0.00858384, -0.0033905501, -0.020739838, 0.016821127, 0.022539245, 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"plot_embedding"}, 38 {"queryVector", queryVector}, 39 {"numCandidates", 150}, 40 {"limit", 10}, 41 }}} 42 43 projectStage := bson.D{ 44 {"$project", bson.D{ 45 {"_id", 0}, 46 {"plot", 1}, 47 {"title", 1}, 48 {"score", bson.D{{"$meta", "vectorSearchScore"}}}, 49 }}} 50 51 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage}) 52 if err != nil { 53 log.Fatalf("failed to retrieve data from the server: %v", err) 54 } 55 // Display the results 56 type ProjectedMovieResult struct { 57 Title string `bson:"title"` 58 Plot string `bson:"plot"` 59 Score float64 `bson:"score"` 60 } 61 62 var results []ProjectedMovieResult 63 if err = cursor.All(ctx, &results); err != nil { 64 log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err) 65 } 66 for _, result := range results { 67 fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score) 68 } 69 }
1 Title: Thrill Seekers 2 Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters. 3 Score: 0.7892671227455139 4 5 Title: About Time 6 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. 7 Score: 0.7843604683876038 8 9 Title: The Time Machine 10 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. 11 Score: 0.7801066637039185 12 13 Title: Crusade in Jeans 14 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... 15 Score: 0.7789170742034912 16 17 Title: Timecop 18 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. 19 Score: 0.7771612405776978 20 21 Title: A.P.E.X. 22 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... 23 Score: 0.7730885744094849 24 25 Title: Men in Black 3 26 Plot: Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history. 27 Score: 0.7712380886077881 28 29 Title: Tomorrowland 30 Plot: Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory. 31 Score: 0.7669923901557922 32 33 Title: Love Story 2050 34 Plot: With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life. 35 Score: 0.7649372816085815 36 37 Title: The Portal 38 Plot: A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ... 39 Score: 0.7640786170959473
次のクエリは、サンプル ベクトル データに対してセマンティック検索を実行する前に、 01年 1 月1955から 1 月01日1975の間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ます。 $and演算子を使用して、指定された日付の論理AND演算を実行します。 次に、string charactersのベクトル埋め込みを使用して、フィルタリングされたドキュメント内のplot_embeddingフィールドで150の最近傍を検索し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、結果にはplot、title、yearフィールドのみを含めます。結果内にドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/bson" 9 "go.mongodb.org/mongo-driver/mongo" 10 "go.mongodb.org/mongo-driver/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(ctx, clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [1536]float64{ 31 0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 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"$and", bson.A{ 40 bson.D{{"year", bson.D{{"$gt", 1955}}}}, 41 bson.D{{"year", bson.D{{"$lt", 1975}}}}, 42 }, 43 }}}, 44 {"queryVector", queryVector}, 45 {"numCandidates", 150}, 46 {"limit", 10}, 47 }}} 48 49 projectStage := bson.D{ 50 {"$project", bson.D{ 51 {"_id", 0}, 52 {"plot", 1}, 53 {"title", 1}, 54 {"year", 1}, 55 {"score", bson.D{{"$meta", "vectorSearchScore"}}}, 56 }}} 57 58 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage}) 59 if err != nil { 60 log.Fatalf("failed to retrieve data from the server: %v", err) 61 } 62 // display the results 63 type ProjectedMovieResult struct { 64 Title string `bson:"title"` 65 Plot string `bson:"plot"` 66 Year int32 `bson:"year"` 67 Score float64 `bson:"score"` 68 } 69 70 var results []ProjectedMovieResult 71 if err = cursor.All(ctx, &results); err != nil { 72 log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err) 73 } 74 for _, result := range results { 75 fmt.Printf("Title: %v \nPlot: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Year, result.Score) 76 } 77 }
1 Title: Peter Pan 2 Plot: In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ... 3 Year: 1960 4 Score: 0.748110830783844 5 6 Title: Chitty Chitty Bang Bang 7 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. 8 Year: 1968 9 Score: 0.7442465424537659 10 11 Title: That Man from Rio 12 Plot: A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues. 13 Year: 1964 14 Score: 0.7416020035743713 15 16 Title: The Little Prince 17 Plot: A pilot, stranded in the desert, meets a little boy who is a prince on a planet. 18 Year: 1974 19 Score: 0.7378944158554077 20 21 Title: The Red Balloon 22 Plot: A red balloon with a life of its own follows a little boy around the streets of Paris. 23 Year: 1956 24 Score: 0.7342712879180908 25 26 Title: Willy Wonka & the Chocolate Factory 27 Plot: A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all. 28 Year: 1971 29 Score: 0.7342107892036438 30 31 Title: Bedknobs and Broomsticks 32 Plot: An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain. 33 Year: 1971 34 Score: 0.7339356541633606 35 36 Title: Pastoral Hide and Seek 37 Plot: A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later. 38 Year: 1974 39 Score: 0.733299970626831 40 41 Title: The Three Musketeers 42 Plot: A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends. 43 Year: 1973 44 Score: 0.7331198453903198 45 46 Title: Frosty 47 Plot: A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together. 48 Year: 1964 49 Score: 0.7318308353424072
Atlas Vector Search は、 1955から1975の範囲のyear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。 プロットには、 1955から1975の間に公開された映画のプロットに、リクエストの結果をまとめたドキュメントが返されます。
Tip
「 その他のフィルターの例 」も参照してください
「 Atlas クラスターのデータに対してセマンティック検索を実行する方法 」では、 sample_mflix.embedded_moviesコレクション内の埋め込みデータに対するセマンティック検索クエリの他の事前フィルターについて説明します。
次のクエリは、 $vectorSearchステージで string時間移動のベクトル埋め込みを使用してplot_embeddingフィールドを検索します。 最大150の最近傍を考慮し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、 フィールドとplottitleフィールドのみを結果に含めます。結果内の各ドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 import com.mongodb.client.MongoClient; 2 import com.mongodb.client.MongoClients; 3 import com.mongodb.client.MongoCollection; 4 import com.mongodb.client.MongoDatabase; 5 import com.mongodb.client.model.search.FieldSearchPath; 6 import org.bson.Document; 7 import org.bson.conversions.Bson; 8 9 import java.util.List; 10 11 import static com.mongodb.client.model.Aggregates.project; 12 import static com.mongodb.client.model.Aggregates.vectorSearch; 13 import static com.mongodb.client.model.Projections.fields; 14 import static com.mongodb.client.model.Projections.include; 15 import static com.mongodb.client.model.Projections.exclude; 16 import static com.mongodb.client.model.Projections.metaVectorSearchScore; 17 import static com.mongodb.client.model.search.SearchPath.fieldPath; 18 import static com.mongodb.client.model.search.VectorSearchOptions.approximateVectorSearchOptions; 19 import static java.util.Arrays.asList; 20 21 public class BasicQuery { 22 public static void main( String[] args ) { 23 // specify connection 24 String uri = "<connection-string>"; 25 26 // establish connection and set namespace 27 try (MongoClient mongoClient = MongoClients.create(uri)) { 28 MongoDatabase database = mongoClient.getDatabase("sample_mflix"); 29 MongoCollection<Document> collection = database.getCollection("embedded_movies"); 30 31 32 // define $vectorSearch query options 33 List<Double> queryVector = (asList(-0.0016261312d, -0.028070757d, -0.011342932d, -0.012775794d, -0.0027440966d, 0.008683807d, -0.02575152d, -0.02020668d, -0.010283281d, -0.0041719596d, 0.021392956d, 0.028657231d, -0.006634482d, 0.007490867d, 0.018593878d, 0.0038187427d, 0.029590257d, -0.01451522d, 0.016061379d, 0.00008528442d, -0.008943722d, 0.01627464d, 0.024311995d, -0.025911469d, 0.00022596726d, -0.008863748d, 0.008823762d, -0.034921836d, 0.007910728d, -0.01515501d, 0.035801545d, -0.0035688248d, -0.020299982d, -0.03145631d, -0.032256044d, -0.028763862d, -0.0071576433d, -0.012769129d, 0.012322609d, 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-0.011982721d, -0.016967745d, -0.0060913274d, -0.007130985d, -0.013109017d, -0.009710136d)); 34 String indexName = "vector_index"; 35 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding"); 36 int limit = 10; 37 int numCandidates = 150; 38 39 // define pipeline 40 List<Bson> pipeline = asList( 41 vectorSearch( 42 fieldSearchPath, 43 queryVector, 44 indexName, 45 limit, 46 approximateVectorSearchOptions(numCandidates)), 47 project( 48 fields(exclude("_id"), include("title"), include("plot"), 49 metaVectorSearchScore("score")))); 50 51 // run query and print results 52 collection.aggregate(pipeline) 53 .forEach(doc -> System.out.println(doc.toJson())); 54 } 55 } 56 }
1 {"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", "score": 0.7892671227455139} 2 {"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", "score": 0.7843604683876038} 3 {"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", "score": 0.7801066637039185} 4 {"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", "score": 0.7789170742034912} 5 {"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", "score": 0.7771612405776978} 6 {"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.", "score": 0.7730885744094849} 7 {"plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title": "Men in Black 3", "score": 0.7712380886077881} 8 {"plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title": "Tomorrowland", "score": 0.7669923901557922} 9 {"plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title": "Love Story 2050", "score": 0.7649372816085815} 10 {"plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title": "The Portal", "score": 0.7640786170959473}
次のクエリは、サンプル ベクトル データに対してセマンティック検索を実行する前に、 01年 1 月1955から 1 月01日1975の間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ます。 $and演算子を使用して、指定された日付の論理AND演算を実行します。 次に、string charactersのベクトル埋め込みを使用して、フィルタリングされたドキュメント内のplot_embeddingフィールドで150の最近傍を検索し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、結果にはplot、title、yearフィールドのみを含めます。結果内にドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 import static com.mongodb.client.model.Aggregates.*; 2 import static com.mongodb.client.model.Projections.*; 3 import static com.mongodb.client.model.search.SearchPath.fieldPath; 4 import static com.mongodb.client.model.search.VectorSearchOptions.vectorSearchOptions; 5 import static java.util.Arrays.asList; 6 7 import com.mongodb.client.MongoClient; 8 import com.mongodb.client.MongoClients; 9 import com.mongodb.client.MongoCollection; 10 import com.mongodb.client.MongoDatabase; 11 import com.mongodb.client.model.*; 12 import com.mongodb.client.model.search.VectorSearchOptions; 13 import java.util.List; 14 import com.mongodb.client.model.search.FieldSearchPath; 15 import org.bson.Document; 16 import org.bson.conversions.Bson; 17 18 public class FilterQuery { 19 public static void main( String[] args ) { 20 // specify connection 21 String uri = "<connection-string>"; 22 23 // establish connection and set namespace 24 try (MongoClient mongoClient = MongoClients.create(uri)) { 25 MongoDatabase database = mongoClient.getDatabase("sample_mflix"); 26 MongoCollection<Document> collection = database.getCollection("embedded_movies"); 27 28 // define $vectorSearch query options 29 List<Double> queryVector = 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30 String indexName = "vector_index"; 31 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding"); 32 int numCandidates = 150; 33 int limit = 10; 34 Bson criteria = Filters.and(Filters.gt("year", 1955), Filters.lt("year", 1975)); 35 VectorSearchOptions options = vectorSearchOptions().filter(criteria); 36 37 // define pipeline 38 List<Bson> pipeline = asList( 39 vectorSearch( 40 fieldSearchPath, 41 queryVector, 42 indexName, 43 numCandidates, 44 limit, 45 options), 46 project( 47 fields(exclude("_id"), include("title"), include("plot"), include("year"), 48 metaVectorSearchScore("score")))); 49 50 // run query and print results 51 collection.aggregate(pipeline) 52 .forEach(doc -> System.out.println(doc.toJson())); 53 } 54 } 55 }
1 {"plot": "In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...", "title": "Peter Pan", "year": 1960, "score": 0.748110830783844} 2 {"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.", "title": "Chitty Chitty Bang Bang", "year": 1968, "score": 0.7442465424537659} 3 {"plot": "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "title": "That Man from Rio", "year": 1964, "score": 0.7416020035743713} 4 {"plot": "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title": "The Little Prince", "year": 1974, "score": 0.7378944158554077} 5 {"plot": "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title": "The Red Balloon", "year": 1956, "score": 0.7342712879180908} 6 {"plot": "A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.", "title": "Willy Wonka & the Chocolate Factory", "year": 1971, "score": 0.7342107892036438} 7 {"plot": "An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.", "title": "Bedknobs and Broomsticks", "year": 1971, "score": 0.7339356541633606} 8 {"plot": "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", "title": "Pastoral Hide and Seek", "year": 1974, "score": 0.733299970626831} 9 {"plot": "A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.", "title": "The Three Musketeers", "year": 1973, "score": 0.7331198453903198} 10 {"plot": "A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.", "title": "Frosty", "year": 1964, "score": 0.7318308353424072}
Atlas Vector Search は、 1955から1975の範囲のyear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。 プロットには、 1955から1975の間に公開された映画のプロットに、リクエストの結果をまとめたドキュメントが返されます。
Tip
「 その他のフィルターの例 」も参照してください
「 Atlas クラスターのデータに対してセマンティック検索を実行する方法 」では、 sample_mflix.embedded_moviesコレクション内の埋め込みデータに対するセマンティック検索クエリの他の事前フィルターについて説明します。
次のクエリは、 $vectorSearchステージで string時間移動のベクトル埋め込みを使用してplot_embeddingフィールドを検索します。 最大150の最近傍を考慮し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、 フィールドとplottitleフィールドのみを結果に含めます。結果内の各ドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding', 22 'queryVector': [ 23 -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, 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0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136 24 ], 25 'numCandidates': 150, 26 'limit': 10 27 } 28 }, { 29 '$project': { 30 '_id': 0, 31 'plot': 1, 32 'title': 1, 33 'score': { 34 '$meta': 'vectorSearchScore' 35 } 36 } 37 } 38 ]; 39 // run pipeline 40 const result = coll.aggregate(agg); 41 42 // print results 43 await result.forEach((doc) => console.dir(JSON.stringify(doc))); 44 } finally { 45 await client.close(); 46 } 47 } 48 run().catch(console.dir);
1 '{"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","score":0.7892671227455139}' 2 '{"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","score":0.7843604683876038}' 3 '{"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","score":0.7801066637039185}' 4 `{"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","score":0.7789170742034912}` 5 '{"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","score":0.7771612405776978}' 6 '{"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.","score":0.7730885744094849}' 7 `{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}` 8 '{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}' 9 '{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}' 10 '{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'
次のクエリは、サンプル ベクトル データに対してセマンティック検索を実行する前に、 01年 1 月1955から 1 月01日1975の間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ます。 $and演算子を使用して、指定された日付の論理AND演算を実行します。 次に、string charactersのベクトル埋め込みを使用して、フィルタリングされたドキュメント内のplot_embeddingフィールドで150の最近傍を検索し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、結果にはplot、title、yearフィールドのみを含めます。結果内にドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding', 22 'filter': { 23 '$and': [ 24 { 25 'year': { 26 '$gt': 1955 27 }, 28 'year': { 29 '$lt': 1975 30 } 31 } 32 ] 33 }, 34 'queryVector': [0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 0.010624337, -0.024236975, -0.0048659067, 0.015153068, -0.000490888, -0.022161031, -0.0024560927, -0.007411252, 0.009745035, -0.01886861, 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0.006518728, -0.0047766543, 0.0055501745, 0.039747052, -0.034061346, 0.049425974, 0.0023883271, -0.0035601775, 0.00863434, -0.003897353, 0.016237319, 0.006436087, -0.00037828952, -0.017797582, -0.019450404, 0.0009809496, 0.0036461244, 0.013176293, 0.0036461244, -0.01094829, -0.018260373, 0.00035246418, 0.012885396, -0.006796402, -0.015972868, 0.027899627, -0.0077021485, 0.027608732, 0.01696456, -0.0014486981, -0.017969476, 0.015642302, -0.00477996, -0.0048890463, -0.020058641, 0.008323609, 0.013017623, -0.01886861, -0.008204606, 0.016303431, -0.010029321, -0.001018138, -0.0332151, 0.010525168, 0.032871313, 0.011549917, 0.010928456, -0.014253933, -0.011384634, 0.00894507, 0.034616694, -0.016872002, -0.010987958, -0.011953205], 35 'numCandidates': 150, 36 'limit': 10 37 } 38 }, { 39 '$project': { 40 '_id': 0, 41 'title': 1, 42 'plot': 1, 43 'year': 1, 44 'score': { 45 '$meta': 'vectorSearchScore' 46 } 47 } 48 } 49 ]; 50 // run pipeline 51 const result = coll.aggregate(agg); 52 53 // print results 54 await result.forEach((doc) => console.dir(JSON.stringify(doc))); 55 } finally { 56 await client.close(); 57 } 58 } 59 run().catch(console.dir);
1 '{"plot":"In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...","title":"Peter Pan","year":1960,"score":0.748110830783844}' 2 '{"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.","title":"Chitty Chitty Bang Bang","year":1968,"score":0.7442465424537659}' 3 '{"plot":"A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.","title":"That Man from Rio","year":1964,"score":0.7416020035743713}' 4 '{"plot":"A boy raised by wolves tries to adapt to human village life.","title":"Jungle Book","year":1942,"score":0.7387760877609253}' 5 '{"plot":"A pilot, stranded in the desert, meets a little boy who is a prince on a planet.","title":"The Little Prince","year":1974,"score":0.7378944158554077}' 6 '{"plot":"A red balloon with a life of its own follows a little boy around the streets of Paris.","title":"The Red Balloon","year":1956,"score":0.7342712879180908}' 7 '{"plot":"A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.","title":"Willy Wonka & the Chocolate Factory","year":1971,"score":0.7342107892036438}' 8 '{"plot":"An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.","title":"Bedknobs and Broomsticks","year":1971,"score":0.7339356541633606}' 9 '{"plot":"Arriving home to find his native land under the yoke of corrupt merchants, an adventurer named Sadko sets sail in search of a mythical bird of happiness.","title":"Sadko","year":1953,"score":0.7339220643043518}' 10 '{"plot":"A young boy’s coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.","title":"Pastoral Hide and Seek","year":1974,"score":0.733299970626831}';
Atlas Vector Search は、 1955から1975の範囲のyear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。 プロットには、 1955から1975の間に公開された映画のプロットに、リクエストの結果をまとめたドキュメントが返されます。
Tip
「 その他のフィルターの例 」も参照してください
「 Atlas クラスターのデータに対してセマンティック検索を実行する方法 」では、 sample_mflix.embedded_moviesコレクション内の埋め込みデータに対するセマンティック検索クエリの他の事前フィルターについて説明します。
次のクエリは、 $vectorSearchステージで string時間移動のベクトル埋め込みを使用してplot_embeddingフィールドを検索します。 最大150の最近傍を考慮し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、 フィールドとplottitleフィールドのみを結果に含めます。結果内の各ドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 8 { 9 '$vectorSearch': { 10 'index': 'vector_index', 11 'path': 'plot_embedding', 12 'queryVector': [-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, 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-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], 13 'numCandidates': 150, 14 'limit': 10 15 } 16 }, { 17 '$project': { 18 '_id': 0, 19 'plot': 1, 20 'title': 1, 21 'score': { 22 '$meta': 'vectorSearchScore' 23 } 24 } 25 } 26 ] 27 28 # run pipeline 29 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 30 31 # print results 32 for i in result: 33 print(i) 34
1 {'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', 'score': 0.7892671227455139} 2 {'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', 'score': 0.7843604683876038} 3 {'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', 'score': 0.7801066637039185} 4 {'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', 'score': 0.7789170742034912} 5 {'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', 'score': 0.7771612405776978} 6 {'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.', 'score': 0.7730885744094849} 7 {'plot': "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 'title': 'Men in Black 3', 'score': 0.7712380886077881} 8 {'plot': 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 'title': 'Tomorrowland', 'score': 0.7669923901557922} 9 {'plot': 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 'title': 'Love Story 2050', 'score': 0.7649372816085815} 10 {'plot': 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 'title': 'The Portal', 'score': 0.7640786170959473}
次のクエリは、サンプル ベクトル データに対してセマンティック検索を実行する前に、 01年 1 月1955から 1 月01日1975の間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ます。 $and演算子を使用して、指定された日付の論理AND演算を実行します。 次に、string charactersのベクトル埋め込みを使用して、フィルタリングされたドキュメント内のplot_embeddingフィールドで150の最近傍を検索し、結果として10ドキュメントを返します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、結果にはplot、title、yearフィールドのみを含めます。結果内にドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 8 { 9 '$vectorSearch': { 10 'index': 'vector_index', 11 'path': 'plot_embedding', 12 'filter': { 13 '$and': [ 14 { 15 'year': { 16 '$gt': 1955 17 }, 18 'year': { 19 '$lt': 1975 20 } 21 } 22 ] 23 }, 24 'queryVector': [0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 0.010624337, -0.024236975, -0.0048659067, 0.015153068, -0.000490888, -0.022161031, -0.0024560927, -0.007411252, 0.009745035, -0.01886861, 0.02112967, -0.011939983, 0.015153068, 0.0025800543, 0.017824028, -0.02410475, -0.016633997, -0.0018214093, -0.008323609, -0.009222744, 0.009388026, -0.0028296304, 0.0017536436, 0.0065517845, -0.011635863, -0.028454976, -0.018934723, 0.012951509, 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-0.022372592, -0.001152843, 0.010002876, 0.0036262905, -0.017876917, 0.006406336, -0.009401249, 0.019569406, -0.03033258, -0.01269367, 0.0020412346, 0.009989654, -0.0014627471, 0.04101642, 0.0011189602, -0.023046944, 0.0013230836, 0.024250198, 0.01207882, -0.0062377485, -0.010452444, -0.020825552, 0.006693927, -0.005305557, -0.018339708, -0.041307315, -0.012296992, 0.0070542423, 0.0019371068, 0.0117945345, -0.020032197, 0.017797582, -0.015443964, 0.00537167, -0.00015474542, -0.0117747, -0.011140017, 0.017334793, 0.016250541, 0.006019576, 0.017612467, -0.017982699, 0.010366497, 0.0029949127, 0.015086955, -0.000027813887, -0.008660785, -0.008713675, -0.0050873845, -0.0117945345, -0.016118316, -0.0022015583, 0.006518728, -0.0047766543, 0.0055501745, 0.039747052, -0.034061346, 0.049425974, 0.0023883271, -0.0035601775, 0.00863434, -0.003897353, 0.016237319, 0.006436087, -0.00037828952, -0.017797582, -0.019450404, 0.0009809496, 0.0036461244, 0.013176293, 0.0036461244, -0.01094829, -0.018260373, 0.00035246418, 0.012885396, -0.006796402, -0.015972868, 0.027899627, -0.0077021485, 0.027608732, 0.01696456, -0.0014486981, -0.017969476, 0.015642302, -0.00477996, -0.0048890463, -0.020058641, 0.008323609, 0.013017623, -0.01886861, -0.008204606, 0.016303431, -0.010029321, -0.001018138, -0.0332151, 0.010525168, 0.032871313, 0.011549917, 0.010928456, -0.014253933, -0.011384634, 0.00894507, 0.034616694, -0.016872002, -0.010987958, -0.011953205], 25 'numCandidates': 150, 26 'limit': 10 27 } 28 }, { 29 '$project': { 30 '_id': 0, 31 'title': 1, 32 'plot': 1, 33 'year': 1, 34 'score': { 35 '$meta': 'vectorSearchScore' 36 } 37 } 38 } 39 ] 40 41 # run pipeline 42 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 43 44 # print results 45 for i in result: 46 print(i) 47
1 {'plot': 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...', 'title': 'Peter Pan', 'year': 1960, 'score': 0.748110830783844} 2 {'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.', 'title': 'Chitty Chitty Bang Bang', 'year': 1968, 'score': 0.7442465424537659} 3 {'plot': 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 'title': 'That Man from Rio', 'year': 1964, 'score': 0.7416020035743713} 4 {'plot': 'A boy raised by wolves tries to adapt to human village life.', 'title': 'Jungle Book', 'year': 1942, 'score': 0.7387760877609253} 5 {'plot': 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.', 'title': 'The Little Prince', 'year': 1974, 'score': 0.7378944158554077} 6 {'plot': 'A red balloon with a life of its own follows a little boy around the streets of Paris.', 'title': 'The Red Balloon', 'year': 1956, 'score': 0.7342712879180908} 7 {'plot': 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.', 'title': 'Willy Wonka & the Chocolate Factory', 'year': 1971, 'score': 0.7342107892036438} 8 {'plot': 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.', 'title': 'Bedknobs and Broomsticks', 'year': 1971, 'score': 0.7339356541633606} 9 {'plot': 'Arriving home to find his native land under the yoke of corrupt merchants, an adventurer named Sadko sets sail in search of a mythical bird of happiness.', 'title': 'Sadko', 'year': 1953, 'score': 0.7339220643043518} 10 {'plot': "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", 'title': 'Pastoral Hide and Seek', 'year': 1974, 'score': 0.733299970626831}
Atlas Vector Search は、 1955から1975の範囲のyear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。 プロットには、 1955から1975の間に公開された映画のプロットに、リクエストの結果をまとめたドキュメントが返されます。
Tip
「 その他のフィルターの例 」も参照してください
「 Atlas クラスターのデータに対してセマンティック検索を実行する方法 」では、 sample_mflix.embedded_moviesコレクション内の埋め込みデータに対するセマンティック検索クエリの他の事前フィルターについて説明します。
ENN の例
次のクエリは、 $vectorSearchステージでstringのベクトル埋め込みを使用してplot_embeddingフィールドを検索します。 完全一致をリクエストし、結果を10ドキュメントのみに制限します。 このクエリでは、次の操作を実行するために$projectステージも指定しています。
_idフィールドを除外し、結果にはplot、title、yearフィールドのみを含めます。結果内にドキュメントのベクトル検索スコアを表示する
scoreという名前のフィールドを追加します。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding", 6 "queryVector": 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7 "exact": true, 8 "limit": 10 9 } 10 }, 11 { 12 "$project": { 13 "_id": 0, 14 "plot": 1, 15 "title": 1, 16 "score": { $meta: "vectorSearchScore" } 17 } 18 } 19 ])
1 [ 2 { 3 plot: 'It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.', 4 title: 'Red Dawn', 5 score: 0.7700583338737488 6 }, 7 { 8 plot: 'A dramatization of the World War II Battle of Iwo Jima.', 9 title: 'Sands of Iwo Jima', 10 score: 0.7581185102462769 11 }, 12 { 13 plot: 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.', 14 title: 'White Tiger', 15 score: 0.750884473323822 16 }, 17 { 18 plot: "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", 19 title: 'P-51 Dragon Fighter', 20 score: 0.749922513961792 21 }, 22 { 23 plot: 'A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.', 24 title: 'When Trumpets Fade', 25 score: 0.7498313188552856 26 }, 27 { 28 plot: 'Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.', 29 title: 'Battletruck', 30 score: 0.7497193217277527 31 }, 32 { 33 plot: 'It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....', 34 title: 'Robot Jox', 35 score: 0.7495121955871582 36 }, 37 { 38 plot: 'During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.', 39 title: 'The Enemy Below', 40 score: 0.746050238609314 41 }, 42 { 43 plot: 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 44 title: 'Saints and Soldiers', 45 score: 0.7435222864151001 46 }, 47 { 48 plot: 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 49 title: 'Saints and Soldiers', 50 score: 0.743497371673584 51 } 52 ]
Not yet available.
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/bson" 9 "go.mongodb.org/mongo-driver/mongo" 10 "go.mongodb.org/mongo-driver/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(ctx, clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [1536]float64{ 31 -0.006954097, -0.009932499, -0.001311474, -0.021280076, -0.014608995, -0.008227668, -0.013274779, -0.0069271433, -0.009521453, -0.030943038, 0.017304381, 0.015094165, -0.010397454, 0.010943269, 0.012230316, 0.025943095, 0.02583528, 0.001368751, 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-0.00016856696, 0.006010712, 0.010781546, -0.0020670246, -0.021873062, -0.011859701, -0.00040346567, 0.008665668, 0.016199274, 0.04204803, -0.036091227, 0.032102056, 0.02043103, 0.008402867, 0.013766687, -0.020201921, 0.0054682656, -0.0037027872, -0.01221684, 0.0030036713, -0.027573805, 0.0054076193, 0.001863186, -0.006357743, -0.029137129, -0.0096697, 0.007675113, -0.0153637035, -0.014878534, 0.0060612503, 0.001300524, 0.030484822, 0.003800495, 0.018908137, 0.01840949, -0.00082461984, -0.030808268, 0.014918964, -0.013254563, -0.0036050796, -0.009319299, -0.011920347, -0.0059062657, -0.020714046, -0.0051347115, 0.02737165, 0.005198727, -0.0059601734, -0.027519897, 0.026967343, 0.0007357563, 0.010431146, 0.017654782, -0.019797614, -0.039137013, 0.033099346, 0.0023129785, -0.011017392, -0.0032816331, -0.020848814, 32 } 33 34 vectorSearchStage := bson.D{ 35 {"$vectorSearch", bson.D{ 36 {"index", "vector_index"}, 37 {"path", "plot_embedding"}, 38 {"queryVector", queryVector}, 39 {"exact", true}, 40 {"limit", 10}, 41 }}} 42 43 projectStage := bson.D{ 44 {"$project", bson.D{ 45 {"_id", 0}, 46 {"plot", 1}, 47 {"title", 1}, 48 {"score", bson.D{{"$meta", "vectorSearchScore"}}}, 49 }}} 50 51 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage}) 52 if err != nil { 53 log.Fatalf("failed to retrieve data from the server: %v", err) 54 } 55 // display the results 56 type ProjectedMovieResult struct { 57 Title string `bson:"title"` 58 Plot string `bson:"plot"` 59 Score float64 `bson:"score"` 60 } 61 62 var results []ProjectedMovieResult 63 if err = cursor.All(ctx, &results); err != nil { 64 log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err) 65 } 66 for _, result := range results { 67 fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score) 68 } 69 }
1 Title: Red Dawn 2 Plot: It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces. 3 Score: 0.7700583338737488 4 5 Title: Sands of Iwo Jima 6 Plot: A dramatization of the World War II Battle of Iwo Jima. 7 Score: 0.7581185102462769 8 9 Title: White Tiger 10 Plot: Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction. 11 Score: 0.750884473323822 12 13 Title: P-51 Dragon Fighter 14 Plot: As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons. 15 Score: 0.749922513961792 16 17 Title: When Trumpets Fade 18 Plot: A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct. 19 Score: 0.7498313188552856 20 21 Title: Battletruck 22 Plot: Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age. 23 Score: 0.7497193217277527 24 25 Title: Robot Jox 26 Plot: It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories.... 27 Score: 0.7495121955871582 28 29 Title: The Enemy Below 30 Plot: During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time. 31 Score: 0.746050238609314 32 33 Title: Saints and Soldiers 34 Plot: Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre. 35 Score: 0.7435222864151001 36 37 Title: Saints and Soldiers 38 Plot: Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre. 39 Score: 0.743497371673584
Not yet available.
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding', 22 'queryVector': [ 23 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24 ], 25 'exact': true, 26 'limit': 10 27 } 28 }, { 29 '$project': { 30 '_id': 0, 31 'plot': 1, 32 'title': 1, 33 'score': { 34 '$meta': 'vectorSearchScore' 35 } 36 } 37 } 38 ]; 39 // run pipeline 40 const result = coll.aggregate(agg); 41 42 // print results 43 await result.forEach((doc) => console.dir(JSON.stringify(doc))); 44 } finally { 45 await client.close(); 46 } 47 } 48 run().catch(console.dir);
1 { 2 "plot":"It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.", 3 "title":"Red Dawn", 4 "score":0.7700583338737488 5 } 6 { 7 "plot":"A dramatization of the World War II Battle of Iwo Jima.", 8 "title":"Sands of Iwo Jima", 9 "score":0.7581185102462769 10 } 11 { 12 "plot":"Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.", 13 "title":"White Tiger", 14 "score":0.750884473323822 15 } 16 { 17 "plot":"As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", 18 "title":"P-51 Dragon Fighter", 19 "score":0.749922513961792 20 } 21 { 22 "plot":"A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.", 23 "title":"When Trumpets Fade", 24 "score":0.7498313188552856 25 } 26 { 27 "plot":"Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.", 28 "title":"Battletruck", 29 "score":0.7497193217277527 30 } 31 { 32 "plot":"It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....", 33 "title":"Robot Jox", 34 "score":0.7495121955871582 35 } 36 { 37 "plot":"During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.", 38 "title":"The Enemy Below", 39 "score":0.746050238609314 40 } 41 { 42 "plot":"Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.", 43 "title":"Saints and Soldiers", 44 "score":0.7435222864151001 45 } 46 { 47 "plot":"Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.", 48 "title":"Saints and Soldiers", 49 "score":0.743497371673584 50 }
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 8 { 9 '$vectorSearch': { 10 'index': 'vector_index', 11 'path': 'plot_embedding', 12 'queryVector': 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13 'exact': True, 14 'limit': 10 15 } 16 }, { 17 '$project': { 18 '_id': 0, 19 'plot': 1, 20 'title': 1, 21 'score': { 22 '$meta': 'vectorSearchScore' 23 } 24 } 25 } 26 ] 27 28 # run pipeline 29 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 30 31 # print results 32 for i in result: 33 print(i) 34
1 { 2 'plot': 'It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.', 3 'title': 'Red Dawn', 4 'score': 0.7700583338737488 5 } 6 { 7 'plot': 'A dramatization of the World War II Battle of Iwo Jima.', 8 'title': 'Sands of Iwo Jima', 9 'score': 0.7581185102462769 10 } 11 { 12 'plot': 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.', 13 'title': 'White Tiger', 14 'score': 0.750884473323822 15 } 16 { 17 'plot': "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", 18 'title': 'P-51 Dragon Fighter', 19 'score': 0.749922513961792 20 } 21 { 22 'plot': 'A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.', 23 'title': 'When Trumpets Fade', 24 'score': 0.7498313188552856 25 } 26 { 27 'plot': 'Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.', 28 'score': 0.7497193217277527 29 } 30 { 31 'plot': 'It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....', 32 'title': 'Robot Jox', 33 'score': 0.7495121955871582 34 } 35 { 36 'plot': 'During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.', 37 'title': 'The Enemy Below', 38 'score': 0.746050238609314} 39 {'plot': 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 'title': 'Saints and Soldiers', 'score': 0.7435222864151001 40 } 41 { 42 'plot': 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 43 'title': 'Saints and Soldiers', 44 'score': 0.743497371673584 45 }