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ベクトル検索クエリの実行

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

Atlas Vector Search で近似最近傍探索をサポートしているのは、MongoDB v6.0.11、または v7.0.2 以降を実行しているクラスターです。また、厳密最近傍探索をサポートしているのは、MongoDB v6.0.16、v7.0.10、またはv7.3.2 以降を実行しているクラスターです。

$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。

注意

Ann 検索の最適な再現率は通常、enn 検索との結果の重複が約90 -95 % であると考えられますが、レイテンシは大幅に低くなります 。これにより、精度とパフォーマンスの適切なバランスが実現できます。 Atlas ベクトル検索でこれを実現するには、クエリ時に numCandidatesパラメータを調整します。

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。

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。

$vectorSearch

$vectorSearchパイプラインには次のプロトタイプ形式があります。

{
"$vectorSearch": {
"exact": true | false,
"filter": {<filter-specification>},
"index": "<index-name>",
"limit": <number-of-results>,
"numCandidates": <number-of-candidates>,
"path": "<field-to-search>",
"queryVector": [<array-of-numbers>]
}
}

$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

exact

ブール値

任意

numCandidatesを省略する場合は、これが必要です。

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。

  • false - Ann検索を実行する場合

  • true - ENN検索を実行する場合

省略した場合、デフォルトは false になります。

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。

filter

ドキュメント

任意

インデックス作成されたフィールドを比較してプレフィルターとして使用する MQL 式。ブール値、date、objectId、numeric、string、UUID の値(これらの型の配列を含む)をフィルタリングできます。

Atlas Vector Search でサポートされる MQL 演算子については、「Atlas Vector Search プレフィルター」を参照してください。

index

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。

limit

数値

必須

結果で返されるドキュメントの数( intのみ)。 numCandidatesを指定する場合、この値はnumCandidatesの値を超えることはできません。

numCandidates

数値

任意

このフィールドは、 exactfalseまたは省略されている場合に必須です。

検索中に使用する最近傍の数。 値は ( <=10000以下である必要があります。 返されるドキュメント数より小さい数値は指定できません( limit )。

精度を高めるために、返されるドキュメント数(limit)の 20 倍以上を指定することをお勧めします。

このオーバーリクエスト パターンは、ANN 検索でレイテンシと再現率のトレードオフを行う推奨方法であり、特定のデータセット サイズとクエリ要件に基づいてこのパラメーターを調整することをお勧めします。

このパラメーターに影響する他の変数の詳細については、「numCandidates の選択」を参照してください。

path

string

必須

検索するインデックス付きのベクトルタイプフィールド。

queryVector

数字の配列

必須

float32 の数値の配列、サブタイプ float32BSON BinData ベクトル、またはサブタイプ int1 または int8BSON BinData ベクトルで、クエリ ベクトルを表します。

サブタイプ float32int8int1を持つ BSON binData ベクトルの生成の詳細については、「事前に量子化されたベクトルを取り込む方法」をご覧ください。

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 dimensions の数と一致しなければなりません。

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。

ベクトルのサブタイプが同じである限り、完全忠実度ベクトルを使用して埋め込みをクエリすることができます。これはサブタイプ float32 を持つbinData ベクトルでのみ可能です。他のサブタイプ(int8 または int1)を使用すると、Atlas Vector Search は結果もエラーも返しません。

$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。

$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

  • $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。

$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。

$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でベクトル検索を可能にするフィルタータイプとしての追加フィールド。

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 では、0 から 1 の固定範囲内(0 は類似性が低く、1 は類似性が高い)で、返される各ドキュメントにスコアが割り当てられます。

スコアは、インデックス定義で指定された「類似性」尺度に従って計算されます。cosine および dotProduct類似性について、Atlas Vector Search はスコアが負にならないように正規化します。

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

このアルゴリズムは、ドキュメントベクトル (v1) とクエリベクトル (v2) の類似度スコアを考慮し、スコアを [-11] の範囲で正規化します。Atlas Vector Search は類似度スコアに1を追加してスコアを[02]の範囲に正規化し、その後2で割って01の間の値を確保します。

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

このアルゴリズムは、ドキュメントベクトル (v1) とクエリベクトル (v2) の類似度スコアを考慮し、スコアを [-11] の範囲で正規化します。Atlas Vector Search は類似度スコアに1を追加してスコアを[02]の範囲に正規化し、その後2で割って01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、 01の間の値を確保します。

score = 1 / (1 + euclidean(v1,v2))

このアルゴリズムは、ユークリッド距離を計算することによってスコアを正規化します。ユークリッド距離は、ドキュメントベクトル(v1)とクエリベクトル(v2)の間の距離であり、その範囲は[0]です。Atlas Vector Search は、距離に1を加算して類似度スコアに変換し、その結果で1を割ることで、値が01の間に収まるようにします。

返される各ドキュメントには、メタデータ形式のスコアが含まれています。このスコアを結果セットと共に含めるには、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。

1db.<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。

$vectorSearch filter オプションは、BSON ブール値、日付、objectId、数値、string、UUID の値(これらのタイプの配列を含む)をマッチングします。

データのフィルタリング基準とするフィールドには、vectorSearch タイプのインデックス定義に含まれるフィルタータイプとしてインデックスを作成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が絞り込まれ、すべてのベクトルが比較対象とならないように徹底するのに役立ちます。

Atlas Vector Search は、次の MQL 演算子で、$vectorSearch filter オプションをサポートしています。

タイプ
MQL 演算子

equals

Range(範囲)

「イン」セット

論理

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。

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。

$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。

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 19992000、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 ] } }
]
}

近似最近傍探索の精度を高めるには、numCandidates 数値を返されるドキュメント数(limit)の 20 倍以上に設定することをお勧めします。たとえば、limit5 件の結果を返すように設定する場合、numCandidates は少なくとも 100 に設定します。

このオーバーリクエスト パターンは、ANN 検索でレイテンシと再現率のトレードオフを行う推奨方法です。ただし、特定のデータセット サイズとクエリ要件に基づいて、このパラメーターを調整することをお勧めします。正確な結果が得られるようにするには、次の変数を考慮してください。

  • インデックスサイズ:コレクションが大きいほど、想起率を維持するためには通常、より大きな numCandidates 値が必要です。数百万のベクトルを含むコレクションは、数千のベクトルを含むコレクションよりもはるかに多くの候補が必要となる可能性があります。

  • 制限値numCandidates はインデックスサイズと高い相関関係があるため、limit の値が低い場合、再現率を維持するには、それに比例して numCandidates の値を高くする必要があります。

  • ベクトル量子化: 量子化ベクトルはストレージコストを削減させますが、精度は低下します。量子化ベクトル(int8 または int1 サブタイプ)を使用する場合、完全精度の float32 ベクトルに匹敵する再現率を維持するには、numCandidates 値の引き上げが必要になることがあります。

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。

以下のクエリは、$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。


➤ [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。


これらの例を実行する前に、次のアクションを実行する必要があります。

注意

mongosh を使用する場合、マシンによってはサンプルコードから queryVector をターミナルに貼り付けるのに時間がかかる場合があります。

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plot title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。

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding",
6 "queryVector": 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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plottitleyear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。

1db.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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21 {
22 "$project": {
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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例

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plot title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。

1using MongoDB.Bson;
2using MongoDB.Bson.Serialization.Attributes;
3using MongoDB.Bson.Serialization.Conventions;
4using MongoDB.Driver;
5
6public 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[BsonIgnoreExtraElements]
48public class EmbeddedMovie
49{
50 [BsonIgnoreIfDefault]
51 public ObjectId Id { get; set; }
52 public string? Title { get; set; }
53 public string? Plot { get; set; }
54 [BsonElement("plot_embedding")]
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plottitleyear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。

1using System.Reflection.Emit;
2using MongoDB.Bson;
3using MongoDB.Bson.Serialization.Attributes;
4using MongoDB.Bson.Serialization.Conventions;
5using MongoDB.Driver;
6using MongoDB.Driver.Search;
7
8public 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[BsonIgnoreExtraElements]
55public class EmbeddedMovie
56{
57 [BsonIgnoreIfDefault]
58 public string Title { get; set; }
59 public string Plot { get; set; }
60 public int Year { get; set; }
61 [BsonElement("plot_embedding")]
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例

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plot title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。

1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/v2/bson"
9 "go.mongodb.org/mongo-driver/v2/mongo"
10 "go.mongodb.org/mongo-driver/v2/mongo/options"
11)
12
13func 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(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, 0.03814744, 0.0065411795, 0.016567878, -0.015621523, 0.022899127, -0.011076353, 0.02841731, -0.002679118, -0.002342562, 0.015341615, 0.01804739, -0.020566562, -0.012989056, -0.002990682, 0.01643459, 0.00042527664, 0.008243952, -0.013715484, -0.004835075, -0.009803439, 0.03129636, -0.021432944, 0.0012087687, -0.015741484, -0.0052016205, 0.00080890034, -0.01755422, 0.004811749, -0.017967418, -0.026684547, -0.014128681, 0.0041386373, -0.013742141, -0.010056688, -0.013268964, -0.0110630235, -0.028337335, 0.015981404, -0.00997005, -0.02424535, -0.013968734, -0.028310679, -0.027750863, -0.020699851, 0.02235264, 0.001057985, 0.00081639783, -0.0099367285, 0.013522214, -0.012016043, -0.00086471526, 0.013568865, 0.0019376953, -0.019020405, 0.017460918, -0.023045745, 0.008503866, 0.0064678704, -0.011509543, 0.018727167, -0.003372223, -0.0028690554, -0.0027024434, -0.011902748, -0.012182655, -0.015714826, -0.0098634185, 0.00593138, 0.018753825, 0.0010146659, 0.013029044, 0.0003521757, -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, 0.015381602, 0.015141681, 0.028817179, -0.019726837, -0.0051283115, -0.011489551, -0.013208984, -0.0047017853, -0.0072309524, 0.01767418, 0.0025658219, -0.010323267, 0.012609182, -0.028097415, 0.026871152, -0.010276617, 0.021912785, 0.0022542577, 0.005124979, -0.0019710176, 0.004518512, -0.040360045, 0.010969722, -0.0031539614, -0.020366628, -0.025778178, -0.0110030435, -0.016221326, 0.0036587953, 0.016207997, 0.003007343, -0.0032555948, 0.0044052163, -0.022046074, -0.0008822095, -0.009363583, 0.028230704, -0.024538586, 0.0029840174, 0.0016044717, -0.014181997, 0.031349678, -0.014381931, -0.027750863, 0.02613806, 0.0004136138, -0.005748107, -0.01868718, -0.0010138329, 0.0054348772, 0.010703143, -0.003682121, 0.0030856507, -0.004275259, -0.010403241, 0.021113047, -0.022685863, -0.023032416, 0.031429652, 0.001792743, -0.005644808, -0.011842767, -0.04078657, -0.0026874484, 0.06915057, -0.00056939584, -0.013995391, 0.010703143, -0.013728813, -0.022939114, -0.015261642, -0.022485929, 0.016807798, 0.007964044, 0.0144219175, 0.016821127, 0.0076241563, 0.005461535, -0.013248971, 0.015301628, 0.0085171955, -0.004318578, 0.011136333, -0.0059047225, -0.010249958, -0.018207338, 0.024645219, 0.021752838, 0.0007614159, -0.013648839, 0.01111634, -0.010503208, -0.0038487327, -0.008203966, -0.00397869, 0.0029740208, 0.008530525, 0.005261601, 0.01642126, -0.0038753906, -0.013222313, 0.026537929, 0.024671877, -0.043505676, 0.014195326, 0.024778508, 0.0056914594, -0.025951454, 0.017620865, -0.0021359634, 0.008643821, 0.021299653, 0.0041686273, -0.009017031, 0.04044002, 0.024378639, -0.027777521, -0.014208655, 0.0028623908, 0.042119466, 0.005801423, -0.028124074, -0.03129636, 0.022139376, -0.022179363, -0.04067994, 0.013688826, 0.013328944, 0.0046184794, -0.02828402, -0.0063412455, -0.0046184794, -0.011756129, -0.010383247, -0.0018543894, -0.0018593877, -0.00052024535, 0.004815081, 0.014781799, 0.018007403, 0.01306903, -0.020433271, 0.009043689, 0.033189073, -0.006844413, -0.019766824, -0.018767154, 0.00533491, -0.0024575242, 0.018727167, 0.0058080875, -0.013835444, 0.0040719924, 0.004881726, 0.012029372, 0.005664801, 0.03193615, 0.0058047553, 0.002695779, 0.009290274, 0.02361889, 0.017834127, 0.0049017193, -0.0036388019, 0.010776452, -0.019793482, 0.0067777685, -0.014208655, -0.024911797, 0.002385881, 0.0034988478, 0.020899786, -0.0025858153, -0.011849431, 0.033189073, -0.021312982, 0.024965113, -0.014635181, 0.014048708, -0.0035921505, -0.003347231, 0.030869836, -0.0017161017, -0.0061346465, 0.009203636, -0.025165047, 0.0068510775, 0.021499587, 0.013782129, -0.0024475274, -0.0051149824, -0.024445284, 0.006167969, 0.0068844, -0.00076183246, 0.030150073, -0.0055948244, -0.011162991, -0.02057989, -0.009703471, 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0.014541878, -0.015701497, -0.029350337, 0.021979429, 0.01332228, -0.026244693, -0.0123492675, -0.003895384, 0.0071576433, -0.035454992, -0.00046984528, 0.0033522295, 0.039347045, 0.0005119148, 0.00476843, -0.012995721, 0.0024042083, -0.006931051, -0.014461905, -0.0127558, 0.0034555288, -0.0074842023, -0.030256703, -0.007057676, -0.00807734, 0.007804097, -0.006957709, 0.017181009, -0.034575284, -0.008603834, -0.005008351, -0.015834786, 0.02943031, 0.016861115, -0.0050849924, 0.014235313, 0.0051449724, 0.0025924798, -0.0025741523, 0.04289254, -0.002104307, 0.012969063, -0.008310596, 0.00423194, 0.0074975314, 0.0018810473, -0.014248641, -0.024725191, 0.0151016945, -0.017527562, 0.0018727167, 0.0002830318, 0.015168339, 0.0144219175, -0.004048667, -0.004358565, 0.011836103, -0.010343261, -0.005911387, 0.0022825818, 0.0073175905, 0.00403867, 0.013188991, 0.03334902, 0.006111321, 0.008597169, 0.030123414, -0.015474904, 0.0017877447, -0.024551915, 0.013155668, 0.023525586, -0.0255116, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136,
32 }
33
34 vectorSearchStage := bson.D{
35 {"$vectorSearch", bson.D{
36 {"index", "vector_index"},
37 {"path", "plot_embedding"},
38 {"queryVector", queryVector},
39 {"numCandidates", 150},
40 {"limit", 10},
41 }}}
42
43 projectStage := bson.D{
44 {"$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}
1Title: Thrill Seekers
2Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.
3Score: 0.7892671227455139
4
5Title: About Time
6Plot: 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.
7Score: 0.7843604683876038
8
9Title: The Time Machine
10Plot: 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.
11Score: 0.7801066637039185
12
13Title: Crusade in Jeans
14Plot: 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...
15Score: 0.7789170742034912
16
17Title: Timecop
18Plot: 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.
19Score: 0.7771612405776978
20
21Title: A.P.E.X.
22Plot: 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...
23Score: 0.7730885744094849
24
25Title: Men in Black 3
26Plot: 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.
27Score: 0.7712380886077881
28
29Title: Tomorrowland
30Plot: 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.
31Score: 0.7669923901557922
32
33Title: Love Story 2050
34Plot: With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.
35Score: 0.7649372816085815
36
37Title: The Portal
38Plot: 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 ...
39Score: 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plottitleyear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。

1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/v2/bson"
9 "go.mongodb.org/mongo-driver/v2/mongo"
10 "go.mongodb.org/mongo-driver/v2/mongo/options"
11)
12
13func 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(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, 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, -0.0032015154, -0.005880739, -0.03115238, 0.012951509, 0.0057749585, 0.009202911, -0.0069352393, 0.00205611, 0.0063732797, 0.0039700773, -0.007100521, -0.0077087595, 0.011596196, -0.010207825, -0.007100521, -0.0051006074, -0.01670011, 0.012773004, -0.035304267, -0.0074971984, 0.0025800543, -0.006118745, 0.030253245, -0.0010751605, 0.039456155, 0.007821151, 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0.011344967, 0.015496855, -0.013738252, -0.0026610426, -0.005923712, -0.007953377, 0.01609187, -0.02698727, 0.011483804, -0.014796059, 0.024408868, 0.009778093, -0.0014437396, 0.007001352, 0.022068473, -0.011701977, -0.00007365386, -0.023377508, 0.012964732, -0.010445832, -0.018114924, -0.04009084, -0.0056427326, 0.0071269665, -0.03300354, 0.028666537, -0.025850128, -0.017440572, 0.007966599, -0.026484812, 0.012409384, -0.0022032112, -0.009778093, -0.005798098, -0.015430742, -0.0028775623, -0.011629253, 0.035304267, -0.03295065, 0.019384291, -0.009513641, 0.017387683, -0.019873526, -0.011113572, -0.012052375, -0.010531778, 0.010459054, -0.034458023, -0.01876283, -0.00026589766, 0.008217828, 0.025202222, 0.0009792967, -0.005712151, 0.005150192, -0.01794303, -0.0048956573, -0.010895399, -0.007345139, -0.005725374, 0.036917422, -0.009447528, 0.042603128, 0.017969476, 0.03094082, -0.0061617186, 0.01459772, 0.0040031336, 0.004340309, 0.01979419, -0.0055799256, 0.020349538, -0.019040504, -0.019648742, 0.019780967, -0.0012842424, 0.03839835, -0.0005590669, -0.023165947, -0.011067293, 0.014015927, 0.012303604, -0.10461699, -0.009315303, 0.00067393796, 0.021195784, -0.017506685, 0.009427694, 0.0045055915, 0.00096194213, 0.015919978, 0.016435657, -0.014095262, 0.0028676454, -0.004730375, -0.0136721395, 0.010306995, -0.0073186937, -0.013401077, -0.0090045715, -0.019344624, 0.009242578, -0.016686887, 0.0007702148, 0.012528387, -0.012025929, -0.022689935, -0.009976431, -0.032236632, 0.02750295, 0.004158499, 0.01855127, 0.002371799, -0.0053320024, 0.007715371, -0.03252753, -0.013500246, 0.011973039, -0.008469057, -0.0022924636, 0.0213809, -0.04842106, 0.018895056, 0.0015858823, 0.007576534, -0.024964217, 0.014994397, 0.0020412346, -0.005249361, 0.0014792753, 0.009348359, -0.03638852, -0.028402084, -0.01084251, -0.019979307, -0.0035304269, 0.0036064566, -0.014994397, 0.017652133, 0.01305729, 0.007907098, 0.006667482, 0.0028676454, -0.020005751, -0.012991177, 0.03001524, -0.00046609566, 0.015615858, -0.02935411, -0.0009925193, 0.033796895, -0.019040504, -0.014901839, 0.009533474, -0.010121879, 0.026458368, -0.038054563, 0.009956597, -0.0030048296, -0.0019519823, 0.016872002, -0.001142926, -0.014941507, -0.02930122, 0.004611372, -0.029512782, 0.030887928, -0.0018015754, 0.010624337, -0.0044791466, -0.007993045, -0.0056790947, 0.0019602464, 0.011173073, 0.0023222142, -0.00022499033, 0.0024511344, 0.015443964, 0.018987615, -0.02349651, -0.008740121, 0.00029730127, -0.004750209, -0.017969476, -0.06442037, 0.006816236, 0.0019833858, 0.0063038613, -0.0054675336, -0.01161603, 0.032818425, -0.030094575, 0.009685534, -0.0012520123, -0.0013090346, 0.0085285595, 0.015959645, -0.0006574098, -0.00688896, -0.019133061, -0.0008057505, 0.009672312, 0.019913195, 0.008145104, -0.012290381, 0.0016139803, 0.026405476, 0.014875393, -0.002168502, 0.012792839, -0.011840814, 0.003464314, 0.0069682957, -0.0073781954, 0.018842166, -0.03165484, -0.017242234, 0.006789791, -0.009130186, -0.012263936, -0.015258849, 0.0036692638, 0.008865735, 0.0272385, -0.004009745, -0.017612467, 0.022584153, -0.023760963, 0.004231223, 0.004287419, -0.020891665, 0.022425482, 0.007986434, -0.0030345803, 0.010459054, 0.013844033, 0.012283769, -0.027899627, -0.006250971, -0.023430398, 0.0122573245, -0.004128748, 0.013830811, -0.016766222, 0.022861827, -0.011192908, 0.03665297, -0.00212057, -0.009031017, -0.024170863, -0.0010230965, -0.0064393925, 0.014015927, -0.005956769, 0.000146378, -0.008436001, 0.010604503, 0.013169682, -0.00516672, 0.0012321784, -0.022332925, -0.0022643656, -0.03993217, 0.02050821, 0.01577453, -0.0043667546, -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,
32 }
33
34 vectorSearchStage := bson.D{
35 {"$vectorSearch", bson.D{
36 {"index", "vector_index"},
37 {"path", "plot_embedding"},
38 {"filter", bson.D{{
39 "$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}
1Title: Peter Pan
2Plot: 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 ...
3Year: 1960
4Score: 0.748110830783844
5
6Title: Chitty Chitty Bang Bang
7Plot: 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.
8Year: 1968
9Score: 0.7442465424537659
10
11Title: That Man from Rio
12Plot: A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.
13Year: 1964
14Score: 0.7416020035743713
15
16Title: The Little Prince
17Plot: A pilot, stranded in the desert, meets a little boy who is a prince on a planet.
18Year: 1974
19Score: 0.7378944158554077
20
21Title: The Red Balloon
22Plot: A red balloon with a life of its own follows a little boy around the streets of Paris.
23Year: 1956
24Score: 0.7342712879180908
25
26Title: Willy Wonka & the Chocolate Factory
27Plot: A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.
28Year: 1971
29Score: 0.7342107892036438
30
31Title: Bedknobs and Broomsticks
32Plot: An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.
33Year: 1971
34Score: 0.7339356541633606
35
36Title: Pastoral Hide and Seek
37Plot: 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.
38Year: 1974
39Score: 0.733299970626831
40
41Title: The Three Musketeers
42Plot: A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.
43Year: 1973
44Score: 0.7331198453903198
45
46Title: Frosty
47Plot: 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.
48Year: 1964
49Score: 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例

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plot title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。

1import com.mongodb.client.MongoClient;
2import com.mongodb.client.MongoClients;
3import com.mongodb.client.MongoCollection;
4import com.mongodb.client.MongoDatabase;
5import com.mongodb.client.model.search.FieldSearchPath;
6import org.bson.Document;
7import org.bson.conversions.Bson;
8
9import java.util.List;
10
11import static com.mongodb.client.model.Aggregates.project;
12import static com.mongodb.client.model.Aggregates.vectorSearch;
13import static com.mongodb.client.model.Projections.fields;
14import static com.mongodb.client.model.Projections.include;
15import static com.mongodb.client.model.Projections.exclude;
16import static com.mongodb.client.model.Projections.metaVectorSearchScore;
17import static com.mongodb.client.model.search.SearchPath.fieldPath;
18import static com.mongodb.client.model.search.VectorSearchOptions.approximateVectorSearchOptions;
19import static java.util.Arrays.asList;
20
21public 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, -0.006621153d, 0.010583182d, 0.024085402d, -0.001623632d, 0.007864078d, -0.021406285d, 0.002554159d, 0.012229307d, -0.011762793d, 0.0051682983d, 0.0048484034d, 0.018087378d, 0.024325324d, -0.037694257d, -0.026537929d, -0.008803768d, -0.017767483d, -0.012642504d, -0.0062712682d, 0.0009771782d, -0.010409906d, 0.017754154d, -0.004671795d, -0.030469967d, 0.008477209d, -0.005218282d, 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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plottitleyear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。

1import static com.mongodb.client.model.Aggregates.*;
2import static com.mongodb.client.model.Projections.*;
3import static com.mongodb.client.model.search.SearchPath.fieldPath;
4import static com.mongodb.client.model.search.VectorSearchOptions.vectorSearchOptions;
5import static java.util.Arrays.asList;
6
7import com.mongodb.client.MongoClient;
8import com.mongodb.client.MongoClients;
9import com.mongodb.client.MongoCollection;
10import com.mongodb.client.MongoDatabase;
11import com.mongodb.client.model.*;
12import com.mongodb.client.model.search.VectorSearchOptions;
13import java.util.List;
14import com.mongodb.client.model.search.FieldSearchPath;
15import org.bson.Document;
16import org.bson.conversions.Bson;
17
18public 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例

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plot title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。

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async 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 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding',
22 'queryVector': [
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24 ],
25 'numCandidates': 150,
26 'limit': 10
27 }
28 }, {
29 '$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}
48run().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plottitleyear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。

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async 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 },
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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}
59run().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例

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plot title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。

この例の実行可能なバージョンを Python エディタとして使用します。

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
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, -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, 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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
29result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
30
31# print results
32for 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plottitleyear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。

この例の実行可能なバージョンを Python エディタとして使用します。

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
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, -0.0032015154, -0.005880739, -0.03115238, 0.012951509, 0.0057749585, 0.009202911, -0.0069352393, 0.00205611, 0.0063732797, 0.0039700773, -0.007100521, -0.0077087595, 0.011596196, -0.010207825, -0.007100521, -0.0051006074, -0.01670011, 0.012773004, -0.035304267, -0.0074971984, 0.0025800543, -0.006118745, 0.030253245, -0.0010751605, 0.039456155, 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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
42result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
43
44# print results
45for 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例

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plottitleyear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。

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding",
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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.
1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/v2/bson"
9 "go.mongodb.org/mongo-driver/v2/mongo"
10 "go.mongodb.org/mongo-driver/v2/mongo/options"
11)
12
13func 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(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, 0.009777515, -0.016024074, -0.013989056, 0.00685639, 0.030242238, -0.023881124, -0.011057823, -0.0056906347, -0.00055929273, -0.03199424, 0.0072168973, -0.023166848, 0.0033490178, -0.024069803, -0.023557678, -0.020862292, 0.007452744, -0.019002475, 0.007850314, -0.032856762, -0.012951332, 0.003005356, -0.003739849, 0.010053792, 0.019541552, 0.007702067, -0.035498243, -0.00918453, -0.0143529335, 0.000249955, -0.011866439, 0.019703276, 0.0076481593, 0.025161434, -0.015714103, -0.0076818517, -0.023180325, -0.0032883717, -0.02315337, 0.015188503, 0.031347346, -0.008739791, 0.01454161, 0.014824626, -0.025107525, 0.0012558816, -0.012803086, -0.013146748, -0.01652272, 0.01283004, -0.019352876, -0.010444623, 0.04706145, 0.030754361, 0.008820653, 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-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}
1Title: Red Dawn
2Plot: 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.
3Score: 0.7700583338737488
4
5Title: Sands of Iwo Jima
6Plot: A dramatization of the World War II Battle of Iwo Jima.
7Score: 0.7581185102462769
8
9Title: White Tiger
10Plot: 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.
11Score: 0.750884473323822
12
13Title: P-51 Dragon Fighter
14Plot: 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.
15Score: 0.749922513961792
16
17Title: When Trumpets Fade
18Plot: 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.
19Score: 0.7498313188552856
20
21Title: Battletruck
22Plot: Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.
23Score: 0.7497193217277527
24
25Title: Robot Jox
26Plot: 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....
27Score: 0.7495121955871582
28
29Title: The Enemy Below
30Plot: 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.
31Score: 0.746050238609314
32
33Title: Saints and Soldiers
34Plot: 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.
35Score: 0.7435222864151001
36
37Title: Saints and Soldiers
38Plot: 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.
39Score: 0.743497371673584
Not yet available.
1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async 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}
48run().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}

この例の実行可能なバージョンを Python エディタとして使用します。

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
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
29result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
30
31# print results
32for 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}

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