뷰 에서 MongoDB Vector Search 인덱스 생성하여 문서 및 컬렉션을 변환하여 컬렉션 부분적으로 인덱스, 호환되지 않는 데이터 유형 또는 데이터 모델 등을 지원 등의 작업을 수행할 수 있습니다.
다음 예시는 sample_mflix 샘플 데이터베이스를 사용합니다.
참고
명확화
이 페이지에서는 표준 보기에 대해 설명합니다. 온디맨드 구체화된 보기에 대한 자세한 내용은 온디맨드 구체화 보기를 참조하세요.
보기 유형 간의 차이점에 대해 알아보려면 온디맨드 구체화 보기의 비교를 참조하세요.
요구 사항:
MongoDB 8.0 이상을 사용해야 합니다.
MongoDB v8.0 이상:
Atlas UI 또는 Atlas 관리 API 사용하여 View에 MongoDB Vector Search 인덱스를 생성합니다.
소스 컬렉션 에 대해 MongoDB Vector Search 쿼리를 실행합니다. 뷰에서 생성된 MongoDB Vector Search 인덱스 참조합니다. 이러한 쿼리는 소스 컬렉션 에 나타나는 원본 문서를 반환합니다.
MongoDB v8.1 이상에서는 추가로 다음을 수행할 수 있습니다.
mongosh및 드라이버 메서드인db.collection.createSearchIndex(),db.collection.updateSearchIndex(),db.collection.dropSearchIndex()및$listSearchIndexes를 사용하여 뷰에서 MongoDB Vector Search 인덱스를 생성하고 관리 .뷰에 대해 MongoDB Vector Search 쿼리를 실행합니다.
제한 사항
MongoDB Vector Search는 다음 단계에서 뷰를 지원합니다.
인덱스 이름은 소스 컬렉션과 모든 보기에서 고유해야 합니다.
MongoDB Vector Search는 $$USER_ROLES 시스템 변수 및 $rand 집계 연산자 와 같이 동적 결과를 생성하는 연산자가 있는 뷰 정의를 지원 하지 않습니다.
MongoDB Vector Search 쿼리는 소스 컬렉션 에 나타나는 원본 문서를 반환합니다.
예시: 문서 필터링
보기를 생성하려면 createCollection 권한이 필요합니다.
문서를 필터링하여 컬렉션을 부분적으로 인덱싱할 수 있습니다. 다음 예시는 sample_mflix.embedded_movies 컬렉션에 벡터 임베딩 필드가 있는 문서만 인덱싱되도록 보기를 생성합니다.
➤ 언어 선택 드롭다운 메뉴를 사용하여 이 페이지의 예시에 사용할 인터페이스 언어를 설정합니다.
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mongosh를 사용하여 클러스터에 연결합니다.
자세한 학습은 mongosh 통해 클러스터에 연결을참조하세요.
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4
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 | Atlas 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. |
중요:
MongoDB Search 인덱스 의 이름은 기본값 으로 default 으로 지정됩니다. 이 이름을 유지하면 해당 인덱스 연산자에 다른 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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embeddingsIndex 부분 인덱스에 쿼리를 실행합니다.
참고
다음 예시는 이름이 moviesWithEmbeddings인 뷰에 대해 .aggregate 명령을 실행하여 embeddingsIndex 인덱스를 쿼리합니다. 클러스터가 MongoDB v8.0을 실행 중인 경우 뷰의 인덱스를 사용하여 소스 컬렉션(예: embedded_movies)을 쿼리해야 합니다. 뷰를 직접 쿼리하려면 MongoDB v8.1+로 업그레이드하세요.
다음 임베딩을
query-embeddings.js라는 파일에 저장합니다.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,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이 파일에는 쿼리 텀
time travel에 대한 임베딩이 포함되어 있습니다.mongosh에서 파일을mongosh에 로드하여 쿼리에 임베딩을 사용합니다.load('/<path-to-file>/query-embeddings.js'); 다음 쿼리를 실행합니다.
1 use sample_mflix 2 db.moviesWithEmbeddings.aggregate([ 3 { 4 "$vectorSearch": { 5 "index": "embeddingsIndex", 6 "path": "plot_embedding_voyage_3_large", 7 "queryVector": TIME_TRAVEL_EMBEDDING, 8 "numCandidates": 100, 9 "limit": 10 10 } 11 }, 12 { 13 "$project": { 14 "_id": 0, 15 "plot": 1, 16 "title": 1, 17 } 18 } 19 ]) [ { 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' }, { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', title: 'Retroactive' }, { 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.' }, { 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' }, { plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.', title: 'Back to the Future Part II' }, { 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' }, { 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, ...', title: 'Timerider: The Adventure of Lyle Swann' }, { 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' }, { plot: 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.', title: "The Time Traveler's Wife" }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', title: 'The Final Countdown' } ]
1
mongosh을 사용하여 MongoDB 배포에 연결합니다.
자세한 학습은 mongosh 통해 클러스터에 연결을참조하세요.
3
4
5
embeddingsIndex 부분 인덱스에 쿼리를 실행합니다.
참고
다음 예시는 이름이 moviesWithEmbeddings인 뷰에 대해 .aggregate 명령을 실행하여 embeddingsIndex 인덱스를 쿼리합니다. 클러스터가 MongoDB v8.0을 실행 중인 경우 뷰의 인덱스를 사용하여 소스 컬렉션(예: embedded_movies)을 쿼리해야 합니다. 뷰를 직접 쿼리하려면 MongoDB v8.1+로 업그레이드하세요.
다음 임베딩을
query-embeddings.js라는 파일에 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이 파일에는 쿼리 텀
time travel에 대한 임베딩이 포함되어 있습니다.mongosh에서 파일을mongosh에 로드하여 쿼리에 임베딩을 사용합니다.load('/<path-to-file>/query-embeddings.js'); 다음 쿼리를 실행합니다.
1 use sample_mflix 2 db.moviesWithEmbeddings.aggregate([ 3 { 4 "$vectorSearch": { 5 "index": "embeddingsIndex", 6 "path": "plot_embedding_voyage_3_large", 7 "queryVector": TIME_TRAVEL_EMBEDDING, 8 "numCandidates": 100, 9 "limit": 10 10 } 11 }, 12 { 13 "$project": { 14 "_id": 0, 15 "plot": 1, 16 "title": 1, 17 } 18 } 19 ]) [ { 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' }, { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', title: 'Retroactive' }, { 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.' }, { 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' }, { plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.', title: 'Back to the Future Part II' }, { 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' }, { 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, ...', title: 'Timerider: The Adventure of Lyle Swann' }, { 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' }, { plot: 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.', title: "The Time Traveler's Wife" }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', title: 'The Final Countdown' } ]
성능 고려 사항
뷰 변환이 매우 복잡하면 Atlas 뷰를 읽어 소스 컬렉션 을 필터하다 하고 변환할 때 성능이 저하될 수 있습니다. 이 시나리오에서는 Atlas 에서 추가 복제 로드를 방지하기 위해 구체화된 뷰 를 생성하는 것이 좋습니다. 뷰 변환으로 인한 쿼리 지연 시간 방지하려면 소스 컬렉션 직접 쿼리 원본 문서를 조회 할 수 있습니다.
문제 해결
인덱스 변경 사항 FAILED
다음 시나리오에서는 인덱스 상태가 FAILED로 변경됩니다.
View에 MongoDB Vector Search와 호환되지 않는 인덱스 생성합니다.
MongoDB Vector Search 호환성 요구 사항을 충족하지 않는 방식으로 뷰를 편집합니다.
보기의 소스 컬렉션을 제거하거나 변경한 경우
예를 들어 하나의 보기가 다른 보기를 기반으로 생성되었고 그 상위 보기 소스를 다른 컬렉션으로 변경하는 경우입니다.
참고
이 제한은 뷰가 다른 뷰의 하위인 경우에도 적용됩니다. 예를 들어, 모든 하위 항목의 출처가 되는 소스 컬렉션을 변경하거나 제거할 수 없습니다.
인덱스 변경 사항 STALE
다음 시나리오에서는 인덱스 상태가 STALE로 변경됩니다.
경고
뷰에 정의된 집계 파이프라인이 컬렉션의 문서와 호환되지 않으면 검색 복제가 실패합니다. 예를 들어, 배열이 포함된 문서 필드에서 $toDouble 표현식이 작동하는 경우 복제가 실패합니다. 뷰가 컬렉션의 모든 문서에서 오류 없이 작동하는지 확인하세요.
인덱스
READY인 동안 뷰 정의로 인해 집계 실패가 발생하면 인덱스STALE이(가) 됩니다. 문서 확인하거나 더 이상 실패하지 않도록 뷰 정의를 변경한 후에는 인덱스READY(으)로 돌아갑니다.STALE인 경우 인덱스 쿼리 가능한 상태로 유지됩니다. 인덱스 oplog에서 떨어지면 인덱스 재작성이 트리거됩니다.인덱스가
BUILDING일 때 뷰 정의가 집계 파이프라인 오류를 일으키면 문서를 수정할 때까지 인덱스 생성이 중단됩니다. 문서를 해결하거나 뷰 정의를 변경하여 더 이상 실패하지 않도록 하면 인덱스가READY로 돌아갑니다.
Atlas UI의 인덱스 상태 세부 정보 페이지에서 인덱스 상태를 볼 수 있습니다.
오류: $search 은(는) 파이프라인의 첫 번째 단계로만 유효합니다.
이 오류는 8.1 이전 MongoDB 버전을 사용하여 뷰를 쿼리 때 나타납니다.
8.0 이전 MongoDB 버전을 사용하는 경우 8.1+로 업그레이드 뷰를 직접 쿼리 것이 좋습니다. 8.0 으로 업그레이드 소스 컬렉션 쿼리 할 수 있습니다.
MongoDB 8.0을 사용하는 경우 소스 컬렉션 에 대해 뷰 인덱스 쿼리 해야 합니다. 예시 를 들어 보기 대신 컬렉션 에서
.aggregate()를 실행 .
인덱스 프로세스
뷰에서 MongoDB Vector Search 인덱스 생성하면 mongot 프로세스 일반 컬렉션 에서 MongoDB Vector Search 인덱스 생성할 때와 동일한 작업을 수행합니다. mongot 프로세스:
컬렉션 에 대한 인덱스 정의의 규칙을 기반으로 MongoDB Vector Search 인덱스를 생성합니다.
MongoDB Vector Search 인덱스를 정의한 컬렉션의 문서 및 인덱스의 현재 상태 에 대한 변경 스트림을 모니터링합니다.
MongoDB Vector Search 쿼리를 처리하고 일치하는 문서에 대한 문서 ID 및 기타 검색 메타데이터
mongod로 반환한 다음, 가 전체 문서 조회를 수행하고 결과를 클라이언트 에 반환합니다.
View에 MongoDB Vector Search 인덱스 생성하면 1 및 2 단계에서 View 정의가 적용되고 변환된 문서는 디스크의 MongoDB Vector Search 인덱스 에 저장됩니다.
자세히 알아보기
뷰에 대한 자세한 내용은 뷰를 참조하세요.
뷰에 MongoDB Search 인덱스 만들려면 MongoDB Search에서 뷰 사용을 참조하세요.