MongoDB ベクトル検索クエリの精度を測定するには、同じクエリ条件を使用して、 SN 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するかを評価します。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。
ユースケース
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
前提条件
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。
手順
$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。
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。EXN クエリとそれに続く ANN クエリを作成して実行します。
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。
MongoDB ベクトル検索インデックスの作成
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
Atlas で、クラスターのSearch & Vector Search ページに移動します。
MongoDB Search 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。
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。
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。
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、Create clusterをクリックしてクラスターを作成してください。詳細については、「 クラスターの作成 」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、Select cluster ドロップダウンから使用するクラスターを選択し、[Go to Atlas 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。
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。
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。
サイドバーで、 Database見出しの下のData Explorerをクリックします。
データベースを展開し、コレクションを選択します。
コレクションのIndexesタブをクリックします。
Atlas Searchページが表示されます。
3
インデックスの設定を開始します。
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。 |
Index Name and Data Source | 以下の情報を指定してください。
|
Configuration Method | For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor. |
重要:
MongoDB Searchインデックスの名前はデフォルトで default です。この名前を維持する場合、インデックスは、演算子に別の index オプションを指定していないMongoDB Search クエリのデフォルトの検索インデックスになります。複数のインデックスを作成する場合は、インデックス間で一貫した記述的な命名規則を維持することをお勧めします。
4
インデックスの定義を指定してください。
例
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_3_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_3_largeフィールドには、Voyage AI の voyage-3-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_3_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。
Similarity MethodドロップダウンからDot Productを選択します。
[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。
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_3_large", 6 "similarity": "dotProduct", 7 "type": "vector", 8 "quantization": "binary" 9 }, 10 { 11 "path": "genres", 12 "type": "filter" 13 } 14 ] 15 }
1
3
db.collection.createSearchIndex() メソッドを実行してインデックスを作成します。
db.embedded_movies.createSearchIndex( "vector_index", "vectorSearch", { "fields": [ { "numDimensions": 2048, "path": "plot_embedding_voyage_3_large", "similarity": "dotProduct", "type": "vector", "quantization": "binary" }, { "path": "genres", "type": "filter" } ] } } )
vector_index
クエリを実行する
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
クエリの埋め込みを準備します。
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。
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2
mongosh を使用してクラスターに接続します。
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
4
データベースに切り替えます。
例
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
ENN クエリを実行します。
例
次のサンプルクエリをコピーしてターミナルに貼り付け、 mongoshを使用して実行します。 ベクトル埋め込みの文字数により、クエリに貼り付けると、 mongoshは若干遅延する場合があります。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding_voyage_3_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: '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: 0.760047972202301 }, { 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: 0.7576861381530762 }, { 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: 0.7576561570167542 }, { 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: 0.7509932518005371 }, { 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: 0.7502642869949341 }, { 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.', genres: [ 'Sci-Fi', 'Adventure', 'Action' ], title: 'The Time Machine', score: 0.7502503395080566 }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', genres: [ 'Action', 'Sci-Fi' ], title: 'The Final Countdown', score: 0.7469133734703064 }, { 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: 0.7468316555023193 }, { 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: 0.7420939207077026 }, { 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: 0.7356286644935608 } ]
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。
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6
ANN クエリを実行します。
例
次のサンプルクエリをコピーしてターミナルに貼り付け、 mongoshを使用して実行します。 ベクトル埋め込みの文字数により、クエリに貼り付けると、 mongoshは若干遅延する場合があります。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding_voyage_3_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: '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: 0.760047972202301 }, { 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: 0.7576861381530762 }, { 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: 0.7576561570167542 }, { 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: 0.7509932518005371 }, { 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: 0.7502642869949341 }, { 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.', genres: [ 'Sci-Fi', 'Adventure', 'Action' ], title: 'The Time Machine', score: 0.7502503395080566 }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', genres: [ 'Action', 'Sci-Fi' ], title: 'The Final Countdown', score: 0.7469133734703064 }, { 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: 0.7468316555023193 }, { 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: 0.7420939207077026 }, { 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: 0.7356286644935608 } ]
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。
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結果を比較する
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 と 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。