쿼리 결과의 정확성을 측정하는 방법

이 페이지의 내용

사용 사례

전제 조건
절차
Atlas Vector Search 인덱스 만들기
쿼리 실행
결과 비교

동일한 쿼리 기준을 사용하여 양자화된 벡터에 대해 ANN검색 결과가 ENN 검색 결과와 얼마나 일치하는지 평가하여 Atlas 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 측정 검색 수 검색.

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 .
mongosh

절차

쿼리 결과의 정확성을 평가하려면 $vectorSearch 다음을 수행해야 합니다.

벡터 필드 및 데이터를 사전 필터링하려는 다른 필드에 Atlas Vector Search 인덱스 를 생성합니다.
벡터의 저장 과 쿼리 속도를 개선하려면 양자화된 벡터를 사용하는 것이 좋습니다. 양자화된 벡터가 없는 경우 vector 유형 필드 를 인덱싱 할 때 자동 양자화를 활성화 할 수 있습니다.
ENN 쿼리 를 구성하고 실행 한 다음 ANN 쿼리 를 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.

Atlas Vector Search 인덱스 만들기

이 섹션에서는 Atlas Vector Search ANN 및 ENN 쿼리를 실행 하기 위한 Atlas Vector Search 인덱스 를 생성하는 방법을 설명합니다.

AtlasGo Atlas 에서 프로젝트 의 Clusters 페이지로 고 (Go) 합니다.

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.

클러스터의 Atlas Search 페이지로 이동합니다.

사이드바, Data Explorer 또는 클러스터 세부 정보 페이지에서 Atlas Search 페이지로 이동할 수 있습니다.

사이드바에서 Services 제목 아래의 Atlas Search를 클릭합니다.
Select data source 드롭다운에서 클러스터를 선택하고 Go to Atlas Search를 클릭합니다.
Atlas Search 페이지가 표시됩니다.

cluster의 Browse Collections 버튼을 클릭합니다.
데이터베이스를 확장하고 컬렉션을 선택합니다.
컬렉션의 Search Indexes 탭을 클릭합니다.
Atlas Search 페이지가 표시됩니다.

클러스터 이름을 클릭합니다.
Atlas Search 탭을 클릭합니다.
Atlas Search 페이지가 표시됩니다.

Atlas Vector Search 인덱스를 생성합니다.

생성하려면 Create Search Index 클릭하세요.

Atlas Vector Search에서 JSON Editor를 선택하고 Next를 클릭합니다.

Index Name을 입력하고 Database and Collection을 설정합니다.

Index Name 필드에 인덱스의 이름을 입력합니다.
참고
인덱스 이름은 인덱스 유형에 관계없이 네임스페이스 내에서 고유해야 합니다.
예시
이 인덱스 의 이름으로 vector_index를 입력합니다. 이 컬렉션 에 vector_index라는 이름의 인덱스 가 이미 있는 경우 인덱스 에 다른 이름을 입력합니다.
Database and Collection 섹션에서 데이터베이스를 찾고 컬렉션을 선택합니다.
예시
Database and Collection 섹션에서 sample_mflix 데이터베이스를 찾고 embedded_movies 컬렉션을 선택합니다.

인덱스 정의를 지정합니다.

예시

이 인덱스 정의는 Atlas Vector Search 인덱스 에서 자동 바이너리 quantization 가 활성화된 상태에서 plot_embedding 필드 를 vector 유형으로, genres 필드 를 filter 유형으로 인덱싱합니다. plot_embedding 필드 에는 OpenAI의 text-embedding-ada-002 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다. 인덱스 정의는 1536 벡터 차원을 지정하고 euclidean 유사성 함수를 사용하여 거리를 측정합니다.

1 {
2   "fields": [
3     {
4       "numDimensions": 1536,
5       "path": "plot_embedding",
6       "similarity": "euclidean",
7       "type": "vector",
8       "quantization": "binary"
9     },
10     {
11       "path": "genres",
12       "type": "filter"
13     }
14   ]
15 }

Next를 클릭하여 인덱스를 검토합니다.

Create Search Index를 클릭합니다.

Atlas에서 인덱스가 작성 중임을 알려주는 모달 창이 표시됩니다.

Close 버튼을 클릭하여 You're All Set! 모달 창을 닫습니다.

상태를 확인합니다.

새로 생성된 인덱스가 Atlas Search 탭에 나타납니다. 인덱스가 빌드되는 동안 Status 필드는 Build in Progress를 읽습니다. 인덱스 빌드가 완료되면 Status 필드에 Active가 표시됩니다.

참고

컬렉션이 클수록 인덱스를 생성하는 데 시간이 더 오래 걸립니다. 인덱스 빌드가 완료되면 이메일 알림을 받게 됩니다.

쿼리 실행

이 섹션에서는 인덱싱된 컬렉션 에 대해 ENN 및 ANN 쿼리를 실행 하는 방법을 보여 줍니다.

을(를)`mongosh` 사용하여 Atlascluster 에 연결합니다.

터미널 창 을 열고 을(를) 사용하여 클러스터 mongosh 에 연결합니다.학습 mongosh 내용은 를 통한 연결을 참조하세요.

데이터베이스 로 전환합니다.

예시

데이터베이스 를 사용합니다. 데이터베이스 로 전환하려면 프롬프트에서 sample_mflix sample_mflix 다음 명령을 mongosh 실행 합니다.

use sample_mflix

switched to db sample_mflix

ENN 쿼리 를 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     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "filter": {
7         "$and": [
8           {
9             "genres": { "$eq": "Action" }
10           }, 
11           {
12             "genres": { "$ne": "Western" }
13           }
14         ]
15       },
16       "queryVector": [-0.0241895392537117, -0.01683979481458664, -0.0017179687274619937, -0.03551029786467552, -0.032710395753383636, 0.026625992730259895, -0.018266668543219566, -0.010210219770669937, -0.01335337944328785, -0.022843431681394577, 0.010095800273120403, 0.008514124900102615, -0.009308327920734882, 0.0010996009223163128, 0.015332155860960484, -0.011024614796042442, 0.030825844034552574, -0.008668927475810051, 0.0013419002061709762, -0.03448725491762161, -0.0006524411146529019, 0.011031344532966614, -0.0005321328062564135, -0.004745026119053364, -0.0017970525659620762, -0.010573668405413628, 0.006505060940980911, -0.014847557991743088, 0.011482290923595428, -0.010277525521814823, -0.0018811841728165746, 0.003577278694137931, -0.010990961454808712, -0.02285689301788807, -0.012559176422655582, -0.009092951193451881, -0.006249300669878721, -0.010842889547348022, -0.013400493189692497, 0.015332155860960484, 0.007827610708773136, 0.012942816130816936, -0.008655466139316559, 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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 thief with a unique code of professional ethics is double-crossed by his crew and left for dead. Assuming a new disguise and forming an unlikely alliance with a woman on the inside, he looks to hijack the score of the crew's latest heist.",
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Parker',
    score: 0.7952193021774292
  },
  {
    plot: 'An adrenaline junkie walks away from a whirlwind romance and embraces a new life as a thief, though he soon finds himself pursued by veteran police officer and engaged in a turf war with a local gangster.',
    genres: [ 'Action', 'Comedy', 'Romance' ],
    title: 'Kick',
    score: 0.7876788377761841
  },
  {
    plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
    genres: [ 'Action', 'Adventure', 'Comedy' ],
    title: "It's a Mad, Mad, Mad, Mad World",
    score: 0.7861514687538147
  },
  {
    plot: 'A group of professional bank robbers start to feel the heat from police when they unknowingly leave a clue at their latest heist.',
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'Heat',
    score: 0.7858744859695435
  },
  {
    plot: 'A romantic and action packed story of three best friends, a group of high end art thieves, who come into trouble when a love-triangle forms between them.',
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'Once a Thief',
    score: 0.7815249562263489
  },
  {
    plot: 'A recently released ex-con and his loyal wife go on the run after a heist goes awry.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'The Getaway',
    score: 0.7795301079750061
  },
  {
    plot: "The story of what happens after a master thief achieves his last big score, when the FBI agent who promised he'd capture him is about to do just that.",
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'After the Sunset',
    score: 0.779007613658905
  },
  {
    plot: 'A group of bank robbers find their multi-million dollar plan interrupted by a hard-boiled detective.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Takers',
    score: 0.7787627577781677
  },
  {
    plot: "After he's shot during a heist in East L.A., an armored truck driver wrestles with rehabilitation and tracking down the man who committed the crime.",
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'The Take',
    score: 0.7776619791984558
  },
  {
    plot: 'While doing a friend a favor and searching for a runaway teenager, a police detective stumbles upon a bizarre band of criminals about to pull off a bank robbery.',
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'No Good Deed',
    score: 0.7773410677909851
  }
]

이 쿼리는 다음 파이프라인 단계를 사용합니다:

`$vectorSearch`	문서를 사전 필터링하여 `Western` 장르가 아닌 `Action` 장르의 영화를 검색 합니다. 문자열 `thrilling heist with twists` 에 벡터 임베딩을 사용하여 `plot_embedding` 필드 에서 가장 가까운 이웃을 검색합니다. 출력을 10 개의 결과로만 제한합니다.
`$project`	결과의 문서에서 `plot`, `title` 및 `genres` 를 제외한 모든 필드를 제외합니다. 문서 결과의 점수를 표시하는 `score` 필드 를 추가합니다.

ANN 쿼리 를 실행합니다.

예시

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "filter": {
7         "$and": [
8           {
9             "genres": { "$eq": "Action" }
10           }, 
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17       "numCandidates": 100,
18       "limit": 10
19     }
20   },
21   {
22     "$project": {    
23       "_id": 0,
24       "plot": 1,
25       "title": 1,
26       "genres": 1,
27       "score": { $meta: "vectorSearchScore" }    
28     }
29   }
30 ])

[
  {
    plot: "A thief with a unique code of professional ethics is double-crossed by his crew and left for dead. Assuming a new disguise and forming an unlikely alliance with a woman on the inside, he looks to hijack the score of the crew's latest heist.",
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Parker',
    score: 0.7952193021774292
  },
  {
    plot: 'An adrenaline junkie walks away from a whirlwind romance and embraces a new life as a thief, though he soon finds himself pursued by veteran police officer and engaged in a turf war with a local gangster.',
    genres: [ 'Action', 'Comedy', 'Romance' ],
    title: 'Kick',
    score: 0.7876788377761841
  },
  {
    plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
    genres: [ 'Action', 'Adventure', 'Comedy' ],
    title: "It's a Mad, Mad, Mad, Mad World",
    score: 0.7861514687538147
  },
  {
    plot: 'A group of professional bank robbers start to feel the heat from police when they unknowingly leave a clue at their latest heist.',
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'Heat',
    score: 0.7858744859695435
  },
  {
    plot: 'A romantic and action packed story of three best friends, a group of high end art thieves, who come into trouble when a love-triangle forms between them.',
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'Once a Thief',
    score: 0.7815249562263489
  },
  {
    plot: 'A recently released ex-con and his loyal wife go on the run after a heist goes awry.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'The Getaway',
    score: 0.7795301079750061
  },
  {
    plot: "The story of what happens after a master thief achieves his last big score, when the FBI agent who promised he'd capture him is about to do just that.",
    genres: [ 'Action', 'Comedy', 'Crime' ],
    title: 'After the Sunset',
    score: 0.779007613658905
  },
  {
    plot: 'A group of bank robbers find their multi-million dollar plan interrupted by a hard-boiled detective.',
    genres: [ 'Action', 'Crime', 'Thriller' ],
    title: 'Takers',
    score: 0.7787627577781677
  },
  {
    plot: "After he's shot during a heist in East L.A., an armored truck driver wrestles with rehabilitation and tracking down the man who committed the crime.",
    genres: [ 'Action', 'Crime', 'Drama' ],
    title: 'The Take',
    score: 0.7776619791984558
  },
  {
    plot: 'A high-tech police surveillance team attempts to take down a gang of ruthless bank robbers.',
    genres: [ 'Action', 'Crime' ],
    title: 'Cold Eyes',
    score: 0.775409996509552
  }
]

이 쿼리는 다음 파이프라인 단계를 사용합니다:

`$vectorSearch`	문서를 사전 필터링하여 `Western` 장르가 아닌 `Action` 장르의 영화로 검색 합니다. 문자열 `thrilling heist with twists` 에 벡터 임베딩을 사용하여 `plot_embedding` 필드 에서 대략적인 가장 가까운 이웃을 검색합니다. 가장 가까운 이웃을 최대 `100` 개까지 고려하지만 출력을 10 개의 결과로만 제한합니다.
`$project`	결과의 문서에서 `plot`, `title` 및 `genres` 를 제외한 모든 필드를 제외합니다. 문서 결과의 점수를 표시하는 `score` 필드 를 추가합니다.

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 는 다르며, 이는 정확하고 대략적인 가장 가까운 이웃 검색 에 약간의 변화가 있음을 반영합니다.

ENN 검색 은 가능한 모든 후보를 검사하고 유사성 점수를 기반으로 쿼리 와 가장 가까운 일치 항목을 반환합니다. ANN 검색 은 근사치를 사용하여 검색 속도를 높이므로 문서 점수가 변경될 수 있습니다.numCandidates ANN 쿼리 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 는 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.

돌아가기

API 리소스

성능 개선

1	{
2	"fields": [
3	{
4	"numDimensions": 1536,
5	"path": "plot_embedding",
6	"similarity": "euclidean",
7	"type": "vector",
8	"quantization": "binary"
9	},
10	{
11	"path": "genres",
12	"type": "filter"
13	}
14	]
15	}