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クエリ結果の精度を測定する方法

同じクエリ条件を使用して、DN 検索の結果が量子化ベクトルに対する ENN 検索の結果とどの程度一致しているかを評価することで、 MongoDB ベクトル検索クエリの精度を測定できます。つまり、ANN 検索の結果と ENN 検索の結果を比較し、ANN 検索結果が ENN 検索の結果に最近傍を含む頻度を測定できます。

次のいずれかの場合は、結果の精度を測定することをお勧めします。

  • 量子化されたベクトル

  • 多数のベクトル

  • 低次元ベクトル

このページの例を試すには、次のものが必要です。

  • オプションでサンプルデータセット を持つクラスター。


言語を選択 ドロップダウン メニューを使用して、インデックスの作成に使用するインターフェースを選択します。


$vectorSearchクエリ結果の精度を評価するには、次の操作を行う必要があります。

  1. MongoDB ベクトル検索インデックスは、ベクトルフィールドと、データを事前にフィルタリングしたい他のフィールドに作成します。

    ベクトルのストレージとクエリの速度を向上させるために、量子化されたベクトルを使用することをお勧めします。 量子化されたベクトルがない場合は、 vector 型フィールド のインデックスを作成するときに自動量化を有効にできます。

  2. EXN クエリとそれに続く ANN クエリを作成して実行します。

  3. Ann クエリの結果と ENN クエリの結果を比較して、結果の類似性と相違を評価します。

このセクションでは、sample_mflix.embedded_moviesコレクション内のデータに対して前述の 3 手順を実行する方法を示します。 サンプルデータセットを使用しない場合は、自分のデータに対して手順を実行できます。

このセクションでは、 MongoDB ベクトル検索 を実行中ためのMongoDB Vector ベクトル検索インデックスを作成する方法を説明します。

1

MongoDB 検索するページには、Search & Vector Search オプションまたは Data Explorer から移動できます。

  1. まだ表示されていない場合は、プロジェクトを含む組織をナビゲーション バーの Organizations メニューで選択します。

  2. まだ表示されていない場合は、ナビゲーション バーの Projects メニューからプロジェクトを選択します。

  3. サイドバーで、 Database見出しの下のSearch & Vector Searchをクリックします。

    • クラスターがない場合は、次をクリックします。

      Create cluster 必要に応じて 1 つのみを作成します。詳細については、クラスターを作成するを参照してください。
    • プロジェクトに複数のクラスターがある場合は、クラスターを選択します

      Select cluster ドロップダウンから使用するものを選択し、Go to Search をクリックします。

    検索とベクトル検索ページが表示されます。

  1. まだ表示されていない場合は、プロジェクトを含む組織をナビゲーション バーの Organizations メニューで選択します。

  2. まだ表示されていない場合は、ナビゲーション バーの Projects メニューからプロジェクトを選択します。

  3. サイドバーで、 Database見出しの下のData Explorerをクリックします。

  4. データベースを展開し、コレクションを選択します。

  5. コレクションのIndexesタブをクリックします。

  6. バナー内の Search and Vector Search リンクをクリックします。

    検索とベクトル検索ページが表示されます。

2
3

ページで次の選択を行い、Next をクリックしてください。

Search Type

Vector Search のインデックスタイプを選択します。

How do you want to set up your vector data?

次のいずれかを選択します。

  • Automated Embedding MongoDB でテキストフィールドのvector embeddingsを生成およびマネージドする場合。

  • Bring your own embeddings データに対してvector embeddingsをすでに生成している場合は 。

How do you want to set up your vector data?

Bring your own embeddingsを選択します。

Index Name and Data Source

以下の情報を指定してください。

  • Index Name: vector_index

  • Database and Collection:

    • sample_mflix
    • movies

Configuration Method

ガイドを利用する場合は、Visual Editor を選択します。

Rawインデックス定義を編集するには、JSON Editor を選択します。

4

このインデックス定義は、 MongoDB ベクトル検索インデックスの で、自動バイナリ quantization が有効になっている plot_embedding_voyage_4_largeフィールドを vector タイプとしてインデックスし、genresフィールドを filter タイプとしてインデックスします。plot_embedding_voyage_4_largeフィールドには、Voyage AI の voyage-4-large 埋め込みモデルを使用して作成された埋め込みが含まれます。インデックス定義では、2048ベクトル次元を指定し、dotProduct 類似度関数を使用して距離を測定します。

Atlasは、ベクトル埋め込みを含むフィールドとその対応するディメンションを自動的に検出します。sample_mflix.embedded_moviesコレクションでplot_embedding_voyage_4_largeフィールドを選択します。

インデックスを設定するには、次の操作を行う必要があります。

  1. Similarity MethodドロップダウンからDot Productを選択します。

  2. [Advanced] をクリックして、ドロップダウンメニューから Binary 量子化を選択します。

  3. Filter Field セクションで、データをフィルタリングするために genres フィールドを指定します。

次のインデックス定義を JSON エディターに貼り付けます。

1{
2 "fields": [
3 {
4 "numDimensions": 2048,
5 "path": "plot_embedding_voyage_4_large",
6 "similarity": "dotProduct",
7 "type": "vector",
8 "quantization": "binary"
9 },
10 {
11 "path": "genres",
12 "type": "filter"
13 }
14 ]
15}
5
6

Atlas は、インデックスが作成中であることを知らせるモーダル ウィンドウを表示します。

7
8

新規作成したインデックスは Search & Vector Search ページに表示されます。インデックスを作成する間、Statusフィールドには Pending と表示されます。インデックスの作成が完了すると、Statusフィールドには Ready と表示されます。

注意

コレクションが大きいほど、インデックスの作成に時間がかかります。インデックスの作成が完了すると、メール通知が届きます。

1

ターミナルで、mongosh から Atlas クラウドホスト型配置またはローカル配置に接続します。接続方法の詳細については、配置への接続を参照してください。

2
use sample_mflix
switched to db sample_mflix
3

このインデックス定義は、 MongoDB ベクトル検索インデックスの で、自動バイナリ quantization が有効になっている plot_embedding_voyage_4_largeフィールドを vector タイプとしてインデックスし、genresフィールドを filter タイプとしてインデックスします。plot_embedding_voyage_4_largeフィールドには、Voyage AI の voyage-4-large 埋め込みモデルを使用して作成された埋め込みが含まれます。インデックス定義では、2048ベクトル次元を指定し、dotProduct 類似度関数を使用して距離を測定します。

db.embedded_movies.createSearchIndex(
"vector_index",
"vectorSearch",
{
"fields": [
{
"numDimensions": 2048,
"path": "plot_embedding_voyage_4_large",
"similarity": "dotProduct",
"type": "vector",
"quantization": "binary"
},
{
"path": "genres",
"type": "filter"
}
]
}
)
vector_index

Atlas UI Search Tester ではMongoDB ベクトル検索クエリを実行できません。mongoshクエリを実行するには、 またはサポートされているドライバーを使用します。

このセクションでは、インデックス付きコレクションに対して EXN クエリと ANN クエリを実行する方法を説明します。

1

次の埋め込みを query-embeddings.js という名前のファイルに保存してください。

TIME_TRAVEL_EMBEDDING=[-0.034731735,0.008558298,-0.0153717,-0.029912498,0.011549547,0.010261648,-0.011964999,-0.023265276,0.010303194,-0.006896493,-0.00054528,0.003926015,-0.025757983,0.027419789,0.001199616,-0.036227357,-0.005297005,0.021935832,0.010303194,-0.019193852,0.025093261,-0.040049512,-0.033900831,-0.011466458,-0.01827986,-0.0153717,0.023265276,0.007727395,0.000114249,0.005317777,-0.043871664,-0.02127111,-0.019609304,0.016368784,-0.004756918,0.003552109,0.006522586,-0.005400868,-0.015620971,-0.034565553,-0.018695312,-0.023099095,0.050851244,-0.034731735,0.004819236,0.022268193,-0.095719993,0.05517194,-0.046198189,-0.036393538,0.007187308,-0.02459472,-0.036725901,0.009472291,0.019027673,0.020938748,-0.011051006,0.027087428,0.04586583,-0.022600554,-0.05517194,0.044204023,0.01213118,0.047859997,-0.03938479,0.002928932,0.002056484,0.019443123,-0.028583053,0.013543714,0.022932915,0.011632638,0.004923099,0.000389486,0.020024756,-0.024096178,-0.022766734,0.011217186,-0.003198975,0.007104218,-0.047195274,-0.013377533,0.013294443,0.024096178,-0.056501385,-0.026755067,-0.008433662,-0.001911076,0.007976666,-0.008101301,-0.014042255,0.008641388,-0.02176965,0.010012378,-0.000607598,-0.024927082,0.024927082,-0.018612221,-0.001184036,0.005567048,0.001324251,-0.019526213,-0.023597637,0.060489718,-0.010178559,-0.019609304,0.004112968,-0.011217186,-0.031574301,-0.008766023,0.005483958,-0.061819162,-0.023431456,-0.040714234,0.015039339,0.026422706,0.016202603,0.004653055,0.041046593,-0.018030589,0.040381871,-0.002638116,0.013045172,0.004216831,0.005650138,0.027419789,0.003926015,-0.028749233,0.004798463,-0.030244859,0.063813329,0.007145763,-0.017448956,0.025591804,-0.045201108,0.010718645,0.002804297,0.014291527,0.04586583,-0.015205519,-0.021603471,-0.035230275,0.00760276,0.033236109,0.016534964,-0.043206941,-0.003115885,-0.026256526,0.005940954,0.016534964,0.024262359,-0.001630647,0.028084511,-0.012795902,0.007270399,0.001381376,-0.009763107,-0.006896493,0.008433662,-0.019360034,0.000386889,0.030411039,0.025591804,0.010469374,0.037722982,-0.001147684,-0.005400868,0.052845411,-0.052513052,0.00768585,-0.004299921,0.00922302,0.011881908,0.012962082,-0.068798743,0.003593654,0.020938748,-0.013792985,-0.034565553,-0.007519669,-0.04021569,-0.020689478,0.006273315,0.046862911,0.006107135,0.002638116,-0.013792985,-0.005400868,-0.020274026,0.007644305,-0.010801735,0.026422706,0.043871664,0.003780607,0.010261648,-0.064145692,0.011881908,-0.009056839,0.009347656,-0.02459472,0.026422706,0.033236109,0.041212775,0.019027673,-0.00315743,0.004424557,0.020689478,-0.0153717,-0.015205519,-0.034897912,0.020274026,0.016867325,0.040714234,-0.022766734,-0.010967916,0.026256526,0.007062673,-0.015953332,-0.007727395,0.031574301,-0.002887387,-0.00614868,0.004569965,0.019027673,0.012878992,0.011798819,0.004258377,-0.019193852,-0.021437289,-0.021603471,0.000301202,-0.051183607,-0.004985416,-0.030078677,0.012629721,0.065142773,-0.031740483,-0.021104928,-0.03938479,-0.003365156,-0.016036423,0.036393538,0.009804652,-0.018612221,0.060489718,-0.003697517,0.000547876,0.063480966,0.02758597,0.010053922,-0.003655972,-0.001485239,0.018362951,0.021104928,-0.003905243,0.019443123,-0.002658889,-0.00380138,-0.013626805,0.035894997,0.035396457,-0.005691683,0.002762751,0.012878992,-0.009596926,-0.009970833,-0.015953332,0.022434372,0.00614868,-0.021188019,0.001557943,-0.020190936,0.009763107,0.017448956,0.006730312,0.005567048,0.019692395,-0.00218112,-0.016867325,0.006854947,0.007976666,0.019193852,0.040880412,0.007353489,-0.02127111,-0.031906664,-0.026755067,-0.017947499,0.040381871,0.042209856,0.00913993,-0.0307434,-0.017781317,-0.015039339,0.03057722,0.017532047,0.0187784,-0.060822077,0.002928932,-0.026422706,-0.005899409,0.039717149,0.026588887,-0.000971118,0.004923099,-0.013626805,0.0187784,-0.031408124,-0.000695881,0.050851244,-0.014457707,-0.007311944,-0.001293092,-0.002139574,-0.019276943,0.00290816,0.019360034,-0.017781317,0.002160347,0.016618054,-0.006522586,0.011798819,0.029247776,-0.02775215,0.010344739,-0.018362951,-0.036725901,-0.015870241,0.015704062,-0.012463541,0.02459472,-0.024096178,0.001152877,-0.031408124,0.025425622,0.027087428,0.00922302,0.034565553,0.015704062,-0.020689478,-0.00517237,-0.014706978,-0.001589101,0.026090344,0.014956249,0.011715728,0.004299921,-0.00913993,0.022434372,-0.03705826,0.048524719,-0.030411039,0.008433662,0.017033506,-0.000511525,-0.031408124,0.005940954,-0.012962082,-0.031574301,0.017448956,0.010178559,-0.011383367,-0.020107845,-0.005151597,0.006647222,0.013128263,0.007145763,0.008059756,-0.045201108,-0.004943871,0.015787151,-0.045201108,-0.020772567,-0.020274026,0.028250692,-0.024262359,-0.004424557,0.009804652,0.000472576,-0.005691683,0.001443693,-0.013294443,0.001412535,0.013211353,-0.01213118,-0.002118802,0.017781317,-0.007353489,-0.031075761,-0.004923099,0.011383367,-0.004486875,-0.010178559,0.016618054,0.014457707,0.023763817,-0.02459472,-0.00388447,0.012546631,-0.007519669,0.015704062,-0.014291527,0.0096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2

ターミナルウィンドウを開き、mongosh を使用してクラスターに接続します。詳細については、 mongosh経由でクラスターに接続する を参照してください。

3

埋め込みをクエリで使用するには、ファイルを mongosh にロードしてください。

load('/<path-to-file>/query-embeddings.js');
4

sample_mflixデータベースを使用します。 sample_mflixデータベースに切り替えるには、mongosh プロンプトで次のコマンドを実行します。

use sample_mflix
switched to db sample_mflix
5

次のクエリは、$vectorSearch ステージを使用してplot_embedding_voyage_4_large フィールドで映画を検索します。このクエリは、 を使用してexact UNIX 検索を実行し、結果を10 ドキュメントのみに制限します。このクエリでは、結果のドキュメントから 、 、$project 以外のすべてのフィールドを除外するためにplot titlegenresステージも指定されています。また、結果内の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も追加されます。

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding_voyage_4_large",
6 "filter": {
7 "$and": [
8 {
9 "genres": { "$eq": "Action" }
10 },
11 {
12 "genres": { "$ne": "Comedy" }
13 }
14 ]
15 },
16 "queryVector": TIME_TRAVEL_EMBEDDING,
17 "exact": true,
18 "limit": 10
19 }
20 },
21 {
22 "$project": {
23 "_id": 0,
24 "plot": 1,
25 "title": 1,
26 "genres": 1,
27 "score": { $meta: "vectorSearchScore" }
28 }
29 }
30])
[
{
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.',
genres: [ 'Action', 'Crime', 'Sci-Fi' ],
title: 'Timecop',
score: 0.7673629522323608
},
{
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...',
genres: [ 'Action', 'Sci-Fi' ],
title: 'A.P.E.X.',
score: 0.7462635636329651
},
{
plot: "My iz budushchego, or We Are from the Future, is a movie about time travel. Four 21st century treasure seekers are transported back into the middle of a WWII battle in Russia. The movie's ...",
genres: [ 'Action', 'Drama', 'Fantasy' ],
title: 'My iz budushchego',
score: 0.7433278560638428
},
{
plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
genres: [ 'Action', 'Crime', 'Drama' ],
title: 'Retroactive',
score: 0.7427071332931519
},
{
plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
genres: [ 'Action', 'Sci-Fi', 'Thriller' ],
title: 'Thrill Seekers',
score: 0.7418472170829773
},
{
plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
genres: [ 'Action', 'Adventure', 'Drama' ],
title: 'Love Story 2050',
score: 0.7339942455291748
},
{
plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...',
genres: [ 'Action', 'Adventure', 'Sci-Fi' ],
title: 'Timerider: The Adventure of Lyle Swann',
score: 0.7168680429458618
},
{
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.',
genres: [ 'Action', 'Adventure', 'Family' ],
title: 'Tomorrowland',
score: 0.7159395217895508
},
{
plot: "Captain Picard and his crew pursue the Borg back in time to stop them from preventing Earth's first contact with an alien species. They also make sure that Zefram Cochrane makes his famous maiden flight at warp speed.",
genres: [ 'Action', 'Adventure', 'Sci-Fi' ],
title: 'Star Trek: First Contact',
score: 0.7109469175338745
},
{
plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.",
genres: [ 'Action', 'Adventure', 'Drama' ],
title: 'Stargate: Continuum',
score: 0.7034971714019775
}
]

このクエリでは、次のパイプライン ステージを使用します。

  • ドキュメントをプレフィルタリングして、Comedy ジャンルではなく Action ジャンルの映画を検索します。

  • 文字列 time travel のベクトル埋め込みを使用して、plot_embedding_voyage_3_largeフィールドで完全最近傍を検索します。

  • 出力を 10 の結果のみに制限します。

  • 結果のドキュメントから、plottitlegenres 以外のすべてのフィールドを除外します。

  • 結果のドキュメントのスコアを表示する score という名前のフィールドを追加します。

6

次のクエリは、$vectorSearch ステージを使用してplot_embedding_voyage_4_large フィールドで映画を検索します。クエリは を使用してnumCandidates ANN 検索を実行し、結果を10 ドキュメントのみに制限します。このクエリでは、結果のドキュメントから 、 、$project 以外のすべてのフィールドを除外するためにplot titlegenresステージも指定されています。また、結果内の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も追加されます。

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding_voyage_4_large",
6 "filter": {
7 "$and": [
8 {
9 "genres": { "$eq": "Action" }
10 },
11 {
12 "genres": { "$ne": "Comedy" }
13 }
14 ]
15 },
16 "queryVector": TIME_TRAVEL_EMBEDDING,
17 "numCandidates": 100,
18 "limit": 10
19 }
20 },
21 {
22 "$project": {
23 "_id": 0,
24 "plot": 1,
25 "title": 1,
26 "genres": 1,
27 "score": { $meta: "vectorSearchScore" }
28 }
29 }
30])
[
{
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.',
genres: [ 'Action', 'Crime', 'Sci-Fi' ],
title: 'Timecop',
score: 0.7673629522323608
},
{
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...',
genres: [ 'Action', 'Sci-Fi' ],
title: 'A.P.E.X.',
score: 0.7462635636329651
},
{
plot: "My iz budushchego, or We Are from the Future, is a movie about time travel. Four 21st century treasure seekers are transported back into the middle of a WWII battle in Russia. The movie's ...",
genres: [ 'Action', 'Drama', 'Fantasy' ],
title: 'My iz budushchego',
score: 0.7433278560638428
},
{
plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
genres: [ 'Action', 'Crime', 'Drama' ],
title: 'Retroactive',
score: 0.7427071332931519
},
{
plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
genres: [ 'Action', 'Sci-Fi', 'Thriller' ],
title: 'Thrill Seekers',
score: 0.7418472170829773
},
{
plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
genres: [ 'Action', 'Adventure', 'Drama' ],
title: 'Love Story 2050',
score: 0.7339942455291748
},
{
plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...',
genres: [ 'Action', 'Adventure', 'Sci-Fi' ],
title: 'Timerider: The Adventure of Lyle Swann',
score: 0.7168680429458618
},
{
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.',
genres: [ 'Action', 'Adventure', 'Family' ],
title: 'Tomorrowland',
score: 0.7159395217895508
},
{
plot: "Captain Picard and his crew pursue the Borg back in time to stop them from preventing Earth's first contact with an alien species. They also make sure that Zefram Cochrane makes his famous maiden flight at warp speed.",
genres: [ 'Action', 'Adventure', 'Sci-Fi' ],
title: 'Star Trek: First Contact',
score: 0.7109469175338745
},
{
plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.",
genres: [ 'Action', 'Adventure', 'Drama' ],
title: 'Stargate: Continuum',
score: 0.7034971714019775
}
]

このクエリでは、次のパイプライン ステージを使用します。

  • ドキュメントを事前にフィルタリングして、Comedy ジャンルではなく Action ジャンルの映画を検索します

  • 文字列 time travel のベクトル埋め込みを使用して、plot_embedding_voyage_3_largeフィールドで最近傍を検索します。

  • 最大 100 の最近傍を考慮しますが、出力は 10 の結果のみに制限されます。

  • 結果のドキュメントから、plottitlegenres 以外のすべてのフィールドを除外します。

  • 結果のドキュメントのスコアを表示する score という名前のフィールドを追加します。

7

次のクエリは、 $vectorSearchステージを使用して フィールドで映画を検索します。クエリは、Annplot_embedding_voyage_4_large50 $rerank検索から最大 のドキュメントを ステージに渡し、クエリに対するセマンティック関連性で結果を再ランク付けします。クエリは、rerank-2.5 モデルを使用して結果を再ランク付けします。クエリには、結果にドキュメントの再ランクスコアを表示するrerankScore という名前のフィールドも追加されています。

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding_voyage_4_large",
6 "filter": {
7 "$and": [
8 {
9 "genres": { "$eq": "Action" }
10 },
11 {
12 "genres": { "$ne": "Comedy" }
13 }
14 ]
15 },
16 "queryVector": TIME_TRAVEL_EMBEDDING,
17 "numCandidates": 100,
18 "limit": 50
19 }
20 },
21 {
22 "$project": {
23 "_id": 0,
24 "plot": 1,
25 "title": 1,
26 "genres": 1,
27 "score": { $meta: "vectorSearchScore" }
28 }
29 },
30 {
31 "$rerank": {
32 "query": {
33 "text": "time travel"
34 },
35 "path": "plot",
36 "numDocsToRerank": 50,
37 "model": "rerank-2.5"
38 }
39 },
40 {
41 "$addFields": {
42 "rerankScore": { "$meta": "score" }
43 }
44 },
45 {
46 "$project": {
47 "_id": 0,
48 "plot": 1,
49 "title": 1,
50 "genres": 1,
51 "score": { $meta: "vectorSearchScore" },
52 "rerankScore": { $meta: "score" }
53 }
54 },
55 { "$limit": 10 }
56])
[
{
plot: 'A detective investigates a mysterious motel room, which acts as a portal to an alternate universe.',
genres: [ 'Action', 'Fantasy', 'Mystery' ],
title: 'The Lost Room',
score: 0.6741988658905029,
rerankScore: 0.5986876487731934
},
{
plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
genres: [ 'Action', 'Crime', 'Drama' ],
title: 'Retroactive',
score: 0.7427071332931519,
rerankScore: 0.5986876487731934
},
{
plot: 'An executed samurai takes an existential journey throughout time, space and eternity in search of bloody vengeance.',
genres: [ 'Action', 'Drama', 'Fantasy' ],
title: 'Izo',
score: 0.6576001644134521,
rerankScore: 0.5986876487731934
},
{
plot: 'An executed samurai takes an existential journey throughout time, space and eternity in search of bloody vengeance.',
genres: [ 'Action', 'Drama', 'Fantasy' ],
title: 'Izo',
score: 0.6576001644134521,
rerankScore: 0.5986876487731934
},
{
plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
genres: [ 'Action', 'Adventure', 'Drama' ],
title: 'Love Story 2050',
score: 0.7339942455291748,
rerankScore: 0.5986876487731934
},
{
plot: 'In an alternate Victorian Age world, a group of famous contemporary fantasy, SF and adventure characters team up on a secret mission.',
genres: [ 'Action', 'Adventure', 'Fantasy' ],
title: 'The League of Extraordinary Gentlemen',
score: 0.6491703987121582,
rerankScore: 0.5986876487731934
},
{
plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.",
genres: [ 'Action', 'Adventure', 'Drama' ],
title: 'Stargate: Continuum',
score: 0.7034971714019775,
rerankScore: 0.5986876487731934
},
{
plot: 'A young woman from the future forces a local gunman to help her stop an impending alien invasion which will wipe out the human race.',
genres: [ 'Action', 'Adventure', 'Sci-Fi' ],
title: 'Returner',
score: 0.6581127643585205,
rerankScore: 0.5986876487731934
},
{
plot: 'An interstellar teleportation device, found in Egypt, leads to a planet with humans resembling ancient Egyptians who worship the god Ra.',
genres: [ 'Action', 'Adventure', 'Sci-Fi' ],
title: 'Stargate',
score: 0.6534044146537781,
rerankScore: 0.5986876487731934
},
{
plot: 'A teenager with teleportation abilities suddenly finds himself in the middle of an ancient war between those like him and their sworn annihilators.',
genres: [ 'Action', 'Adventure', 'Sci-Fi' ],
title: 'Jumper',
score: 0.6926636695861816,
rerankScore: 0.5986876487731934
}
]

例のENN および ANN クエリ結果の上位 9 ドキュメントは同じであり、スコアも同じです。これは、クエリの上位結果の類似性が高いことを示しています。 ただし、ENT クエリ結果と ANN クエリ結果の 10 番目のドキュメントは異なります。これは、正確な最近傍検索と近似近傍検索のわずかな違いを反映します。

ENN 検索は、可能なすべての候補を検索し、類似性スコアに基づいてクエリに最も近い一致を返します。Ann 検索では近似値を使用して検索を高速化するため、ドキュメントのスコアが変更される可能性があります。ANN numCandidatesクエリで の値を増やすと、結果は ENN クエリ結果とより一致します。ただし、これにより追加の計算リソースが消費され、クエリ速度が低下する可能性があります。 結果の 10 番目のドキュメントは、精度と速度の間のトレードオフを反映します。

ERN フィールドの真実の結果を量的に評価した後、同じ方法で一連の100 クエリをテストし、結果セット間の「ジャーカード類似性」を計算することをお勧めします。Jaccard 類似性は、2 つのセット間の共通部分、つまり重複する項目をセットの合計サイズで割ることで計算できます。 これにより、量子化されたベクトルに対して実行されるクエリを含む、ANN クエリの再現率パフォーマンスが向上します。

ENN ANN numCandidatesのクエリ結果の間に大きな差がある場合は、アプリケーションの精度と速度の理想的なバランスをとるために、 の値を調整することをお勧めします。

理想的な結果を持つ構造化されたクエリリストには、近似最近傍探索クエリまたは厳密最近傍探索グラウンドトゥルース値に対して判断リストを使用することをお勧めします。厳密最近傍探索クエリの結果を基準判定リストとして使用し、この判定リストに対して近似最近傍探索クエリの結果を評価して、再現率、重複率、パフォーマンスを測定します。判断リストは、近似最近傍探索クエリが厳密最近傍探索ベースラインと比較して、望ましい精度または再現率のしきい値を満たしているかどうかを評価する手段を提供します。LLM を使用してクエリ例を生成します。

$vectorSearchの結果の精度を向上させるには、クエリに関連するセマンティックな関連性で結果を再ランク付けします。再ランク付けすると、結果が最も関連性の高いドキュメントを優先するように並べ替えられます。リランク モデルを使用して結果を並べ替えるには、$rerank$vectorSearch ステージの後に ステージを適用します。

詳しくは、$rerank 集計パイプライン ステージ を参照してください。