동일한 쿼리 기준을 사용하여 ANN 검색 결과가 양자화된 벡터에 대해 ENN 검색 결과와 얼마나 일치하는지 평가하여 MongoDB Vector Search 쿼리 의 정확성을 측정할 수 있습니다. 즉, ANN 검색 결과를 ENN 검색 결과와 비교하여 ANN 검색 결과에 ENN 검색 결과에 가장 가까운 이웃이 포함되는 빈도를 측정할 수 있습니다.
사용 사례
다음 중 하나에 해당하는 경우 결과의 정확도를 측정할 수 있습니다.
양자화된 벡터
많은 수의 벡터
저차원 벡터
전제 조건
이 페이지의 예제를 시도하려면 다음이 필요합니다.
선택적으로 샘플 데이터 세트가 있는 Atlas cluster .
➤ 언어 선택 드롭다운 메뉴를 사용하여 인덱스 만드는 데 사용할 인터페이스를 선택합니다.
절차
쿼리 결과의 정확성을 평가하려면 $vectorSearch 다음을 수행해야 합니다.
벡터 필드 와 데이터를 사전 필터링하려는 다른 필드에 MongoDB Vector Search 인덱스 생성합니다.
벡터의 저장 과 쿼리 속도를 개선하려면 양자화된 벡터를 사용하는 것이 좋습니다. 양자화된 벡터가 없는 경우
vector유형 필드 인덱싱 때 자동 양자화를 활성화 할 수 있습니다.ENN 쿼리 구성하고 실행 한 다음 ANN 쿼리 실행합니다.
ANN 쿼리 의 결과와 ENN 쿼리 의 결과를 비교하여 결과의 유사점과 차이점을 평가합니다.
이 섹션에서는 sample_mflix.embedded_movies 컬렉션 의 데이터에 대해 앞의 3 단계를 수행하는 방법을 보여 줍니다. 샘플 데이터 세트를 사용하지 않으려면 자체 데이터에 대해 절차를 수행할 수 있습니다.
MongoDB Vector Search 인덱스 만들기
이 섹션에서는 MongoDB Vector Search ANN 및 ENN 쿼리를 실행 위한 MongoDB Vector Search 인덱스 생성하는 방법을 설명합니다.
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AtlasGo Atlas 에서 프로젝트 의 Clusters 페이지로 고 (Go) 합니다.
경고: 탐색 개선 진행 중 현재 새롭고 향상된 탐색 환경을 도입하고 있습니다. 다음 단계가 Atlas UI 의 보기와 일치하지 않는 경우 미리 보기 설명서를 참조하세요.
아직 표시되지 않은 경우 탐색 표시줄의 Organizations 메뉴에서 원하는 프로젝트가 포함된 조직을 선택합니다.
아직 표시되지 않은 경우 탐색 표시줄의 Projects 메뉴에서 원하는 프로젝트를 선택합니다.
아직 표시되지 않은 경우 사이드바에서 Clusters를 클릭합니다.
Clusters(클러스터) 페이지가 표시됩니다.
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인덱스 구성을 시작합니다.
페이지에서 다음 항목을 선택한 후 Next를 클릭합니다.
Search Type | Vector Search 인덱스 유형을 선택합니다. |
Index Name and Data Source | 다음 정보를 지정합니다.
|
Configuration Method | For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor. |
참고
MongoDB Search 인덱스 의 이름은 기본값 으로 으로 지정됩니다. 이 이름을 유지하면 해당 인덱스 연산자에 다른 default index 옵션을 지정하지 않는 모든 MongoDB Search 쿼리 대한 기본값 검색 인덱스 됩니다. 여러 인덱스를 생성하는 경우 인덱스 전체에서 일관적인 되고 설명이 포함된 명명 규칙을 유지하는 것이 좋습니다.
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인덱스 정의를 지정합니다.
예시
이 인덱스 정의는 MongoDB Vector Search 인덱스 에서 자동 바이너리 quantization 가 활성화된 상태에서 plot_embedding_voyage_3_large 필드 vector 유형으로, genres 필드 filter 유형으로 인덱싱합니다. plot_embedding_voyage_3_large 필드 에는 Voyage AI의 voyage-3-large 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다. 인덱스 정의는 2048 벡터 차원을 지정하고 dotProduct 유사성 함수를 사용하여 거리를 측정합니다.
Atlas는 벡터 임베딩이 포함된 필드와 해당 차원을 자동으로 감지합니다. sample_mflix.embedded_movies 컬렉션에서는 plot_embedding_voyage_3_large 필드를 선택하세요.
인덱스를 구성하려면 다음을 수행합니다.
Similarity Method 드롭다운 메뉴에서 Dot Product을(를) 선택합니다.
Advanced를 클릭한 후 드롭다운 메뉴에서 Binary 양자화를 선택합니다.
Filter Field 섹션에서 데이터를 필터링할
genres필드를 지정합니다.
JSON 편집기에 다음 인덱스 정의를 붙여넣습니다.
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 }
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db.collection.createSearchIndex() 메서드를 실행하여 인덱스 만듭니다.
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
쿼리 실행
Atlas UI 에서는 MongoDB Vector Search 쿼리를 Search Tester 실행 수 없습니다. 또는 지원되는 운전자 사용하여 쿼리 실행 .mongosh
이 섹션에서는 인덱싱된 컬렉션 에 대해 ENN 및 ANN 쿼리를 실행 방법을 보여 줍니다.
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쿼리 임베딩을 준비하세요.
다음 임베딩을 query-embeddings.js라는 파일에 저장합니다.
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2
을(를)mongosh 사용하여 Atlascluster 에 연결합니다.
터미널 창 열고 mongosh을(를) 사용하여 클러스터에 연결합니다. 자세한 학습 은 mongosh 통해 클러스터에 연결을참조하세요.
3
4
데이터베이스 로 전환합니다.
예시
sample_mflix 데이터베이스 사용합니다. 데이터베이스 로 sample_mflix mongosh 전환하려면 프롬프트에서 다음 명령을 실행 .
use sample_mflix
switched to db sample_mflix
5
ENN 쿼리 실행합니다.
예시
다음 샘플 쿼리를 터미널에 복사하여 붙여넣은 다음 mongosh를 사용하여 실행하세요. mongosh 벡터 임베딩에 문자 수로 인해 쿼리를 붙여넣을 때 약간 지연될 수 있습니다.
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 } ]
이 쿼리는 다음 파이프라인 단계를 사용합니다:
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6
ANN 쿼리 실행합니다.
예시
다음 샘플 쿼리를 터미널에 복사하여 붙여넣은 다음 mongosh를 사용하여 실행하세요. mongosh 벡터 임베딩에 문자 수로 인해 쿼리를 붙여넣을 때 약간 지연될 수 있습니다.
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 } ]
이 쿼리는 다음 파이프라인 단계를 사용합니다:
| |
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결과 비교
예시 ENN 및 ANN 쿼리 결과의 상위 9개 문서는 동일하며 점수도 동일합니다. 이는 쿼리 에 대한 상위 결과에서 높은 수준의 유사성을 보여줍니다. 그러나 ENN 및 ANN 쿼리 결과의 10번째 문서 다르며, 이는 정확하고 대략적인 가장 가까운 이웃 검색 에 약간의 변화가 있음을 반영합니다.
ENN 검색 가능한 모든 후보를 검사하고 유사성 점수를 기반으로 쿼리 와 가장 가까운 일치 항목을 반환합니다. ANN 검색 근사치를 사용하여 검색 속도를 높이므로 문서 점수가 변경될 수 있습니다. ANN 쿼리 numCandidates 에서 값을 늘리면 결과가 ENN 쿼리 결과에 더 가깝습니다. 그러나 이렇게 하면 추가 계산 리소스가 소비되고 쿼리 속도가 느려질 수 있습니다. 결과의 열 번째 문서 정확성과 속도 간의 균형을 반영합니다.
ENN 실제 정보와 비교하여 결과를 양적으로 평가한 후에는 100 동일한 방식으로 쿼리 설정하다 를 테스트하고 결과 세트 간의 "jaccard 유사성"을 계산하는 것이 좋습니다. 자카드 유사성은 두 세트의 교집합, 즉 겹치는 항목을 전체 설정하다 크기로 나누어 계산할 수 있습니다. 이를 통해 양자화된 벡터에 대해 수행된 쿼리를 포함하여 ANN 쿼리의 리콜 성능을 파악할 수 있습니다.
ENN 쿼리 결과와 ANN 쿼리 결과 간에 큰 차이가 있는 경우,numCandidates 애플리케이션 의 정확도와 속도 사이의 이상적인 균형을 유지하도록 값을 조정하는 것이 좋습니다.
근사 최근접 이웃 쿼리 또는 등가 최근접 이웃 실제 정답 값에 대한 이상적인 결과가 포함된 구조화된 쿼리 목록을 위해 판단 목록을 사용하는 것이 좋습니다. 등가 최근접 이웃 쿼리 결과를 기준 판단 목록으로 사용한 다음, 이 판단 목록을 기준으로 근사 최근접 이웃 쿼리 결과를 평가하여 재현율, 중복 및 성능을 측정할 수 있습니다. 판단 목록은 근사 최근접 이웃 쿼리가 등가 최근접 이웃 기준선과 비교하여 원하는 정확도나 재현율 임계값을 충족하는지 평가하는 방법을 제공합니다. LLM을 사용하여 예시 쿼리를 생성하세요.