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Vector Search 쿼리 실행

이 페이지의 내용

  • 정의
  • ANN 검색
  • ENN 검색
  • 구문
  • 필드
  • 행동
  • Atlas Vector Search 인덱스
  • Atlas Vector Search 점수
  • Atlas Vector Search 사전 필터링
  • 제한 사항
  • 지원되는 클라이언트
  • 세그먼트 전체에서 쿼리 실행 병렬화
  • 예제
  • ANN 예시
  • ENN 예제

Atlas Vector Search 쿼리는 집계 파이프라인 단계 의 형태를 취합니다. $vectorSearch 쿼리의 경우 Atlas Vector Search 는 시맨틱 Atlas Search의 결과를 반환합니다.

참고

Atlas Vector Search는 MongoDB v6 를 실행하는 클러스터에서 ANN 검색을 지원합니다.0.11, v7.0.2 이상이며 MongoDB v6 를 실행하는 클러스터에서 ENN 검색을 수행합니다.0.16, v7.0.10, v7.3.2 이상입니다.

$vectorSearch 단계는 지정된 필드의 벡터에 대해 ANN 또는 ENN 검색을 수행합니다.

ANN 검색의 경우, Atlas Vector Search는 다차원 공간에서의 근접성과 고려하는 이웃 수를 기반으로 쿼리의 벡터 임베딩에 가장 가까운 데이터의 벡터 임베딩을 찾습니다.Hierarchical Navigable Small Worlds 를 사용합니다. 알고리즘을 사용하여 모든 벡터를 스캔하지 않고도 쿼리에서 벡터 임베딩과 가장 유사한 벡터 임베딩을 찾습니다. 따라서ANN 검색은 중요한 필터링 없이 대규모 데이터 세트를 쿼리하는 데 이상적입니다.

ENN 검색의 경우, Atlas Vector Search는 모든 임베딩 사이의 거리를 계산하여 인덱싱된 모든 벡터 임베딩을 철저하게 검색하고 쿼리에서 벡터 임베딩에 대해 가장 가까운 이웃을 찾습니다. 이는 계산 집약적이며 쿼리 지연 시간에 부정적인 영향을 미칠 수 있습니다. 따라서 다음과 같은 사용 사례에 대해 ENN 을 검색할 것을 권장합니다.

  • ENN 쿼리에 대한 이상적이고 정확한 결과를 사용하여 ANN 쿼리의 재현율과 정확성을 결정하려고 합니다.

  • 고려할 가장 가까운 이웃의 수를 조정할 필요 없이 10000 개 미만의 문서를 쿼리하려고 합니다.

  • 데이터의 5% 미만이 지정된 사전 필터를 충족하는 컬렉션에 대해 쿼리에 선택적 사전 필터를 포함하려고 합니다.

검색하려는 필드는 vectorSearch 인덱스 유형 내에서 Atlas Vector Search 벡터 유형으로 인덱싱되어야 합니다.

$vectorSearch

$vectorSearch 파이프라인의 프로토타입 형식은 다음과 같습니다.

{
"$vectorSearch": {
"exact": true | false,
"filter": {<filter-specification>},
"index": "<index-name>",
"limit": <number-of-results>,
"numCandidates": <number-of-candidates>,
"path": "<field-to-search>",
"queryVector": [<array-of-numbers>]
}
}

$vectorSearch 단계에서는 다음 필드가 있는 문서를 사용합니다.

필드
유형
필요성
설명
exact
부울
옵션

numCandidates 이 생략된 경우 필수입니다.

ENN 또는 ANN 검색을 실행할지 여부를 지정하는 플래그입니다. 값은 다음 중 하나일 수 있습니다.

  • false - ANN 검색을 실행합니다.

  • true - ENN 검색을 실행합니다.

생략하는 경우 기본값은 false입니다.

ENN 검색은 MongoDB 버전 6 를 실행하는 Atlas 클러스터에서 지원됩니다.0.16, 7.0.10, 7.32 이상입니다.

filter
문서
옵션
인덱싱된 필드를 부울, 날짜, objectId, 숫자(소수점 제외) 또는 문자열과 비교하여 사전 필터로 사용하는 모든 MQL 일치 표현식입니다. Atlas Vector Search가 필터에서 지원하는 쿼리집계 파이프라인 연산자를 알아보려면 Atlas Vector Search 사전 필터를 참조하세요.
index
문자열
필수 사항

사용할 Atlas Vector Search 인덱스의 이름입니다.

인덱스 이름의 철자가 틀렸거나 지정된 인덱스가 클러스터에 존재하지 않는 경우 Atlas Vector Search는 결과를 반환하지 않습니다.

limit
숫자
필수 사항
결과에 반환할 문서 수( int 유형에만 해당)입니다. numCandidates 를 지정하는 경우 이 값은 numCandidates 값을 초과할 수 없습니다.
numCandidates
숫자
옵션

이 필드는 exactfalse 이거나 생략된 경우 필수입니다.

검색 중에 사용할 가장 가까운 이웃의 수입니다. 값은 (<=)10000보다 작거나 같아야 합니다. 반환할 문서 수보다 작은 숫자는 지정할 수 없습니다(limit).

지연 시간에 영향을 줄 수 있지만, 반환할 문서 수(limit)보다 큰 수를 지정하여 정확도를 높이는 것이 좋습니다. 예를 들어 문서 한 개로 제한하는 경우 가장 가까운 이웃의 비율을 10~20으로 설정하는 것이 좋습니다. 이러한 과다 요청 패턴은 ANN 검색에서 지연 시간과 리콜의 균형을 맞추는 데 권장되는 방법이며, 특정 데이터 세트에 맞게 조정하는 것이 좋습니다.

path
문자열
필수 사항
검색할 인덱스 vectorEmbedding 유형 필드입니다. 자세한 내용은 경로 구성을 참조하세요.
queryVector
숫자 배열
필수 사항

쿼리 벡터를 나타내는 BSON double 유형의 숫자 배열입니다. 배열 크기는 필드에 대한 인덱스 정의에 지정된 벡터 dimensions 의 수와 일치해야 합니다.

참고

데이터를 임베드할 때 사용한 것과 동일한 모델을 사용하여 쿼리를 임베드해야 합니다.

$vectorSearch 표시되는 파이프라인의 첫 번째 단계에 있어야 합니다.

vectorSearch 유형 인덱스 $vectorSearch 정의 내의 단계를 사용하여 검색할 필드를 인덱싱해야 합니다. Atlas Vector Search vectorSearch 유형 인덱스 정의에서 다음 유형의 필드를 인덱싱할 수 있습니다.

  • 벡터 유형으로 벡터 임베딩을 포함하는 필드입니다.

  • 사전 필터링된 데이터에서 벡터 검색을 활성화하기 위해 필터 유형으로 부울, 날짜, objectId, 숫자 및 문자열 값을 포함하는 필드입니다.

이러한 Atlas Vector Search 필드 유형에 대해 자세히 알아보려면 벡터 검색을 위해 필드를 인덱싱하는 방법을 참조하세요.

Atlas Vector Search는 반환하는 모든 문서에만 0~1 사이의 고정된 범위의 점수를 할당합니다. cosinedotProduct 유사도의 경우, Atlas Vector Search는 다음 알고리즘을 사용하여 점수를 정규화합니다.

score = (1 + cosine/dot_product(v1,v2)) / 2

반환된 문서에 할당된 점수는 문서 메타데이터의 일부입니다. 반환된 각 문서의 점수를 결과 집합과 함께 포함하려면 집계 파이프라인에서 $project 단계를 사용하세요.

Atlas Vector Search 쿼리 결과의 점수를 조회하려면 $meta 표현식의 값으로 vectorSearchScore를 사용합니다. 즉, $vectorSearch 단계 이후 $project 단계에서 score 필드는 $meta 표현식을 사용합니다. 벡터 검색을 위한 문서 점수를 반환하려면 표현식에 vectorSearchScore 값이 필요합니다.

예제

1db.<collection>.aggregate([
2 {
3 "$vectorSearch": {
4 <query-syntax>
5 }
6 },
7 {
8 "$project": {
9 "<field-to-include>": 1,
10 "<field-to-exclude>": 0,
11 "score": { "$meta": "vectorSearchScore" }
12 }
13 }
14])

참고

데이터를 사전 필터링해도 Atlas Vector Search가 $vectorSearch 쿼리에 대해 $vectorSearchScore를 사용하여 반환되는 점수에는 영향을 주지 않습니다.

$vectorSearch filter 옵션은 BSON 부울, 날짜, objectId, 문자열 및 숫자 값만 일치합니다. 데이터를 필터링하려는 필드를 vectorSearch 유형 인덱스 정의의 필터 유형으로 인덱싱 해야 합니다. 데이터 필터링은 시맨틱 검색의 범위를 좁히고 모든 벡터가 비교 대상으로 고려되지 않도록 하는 데 유용합니다.

Atlas Vector Search는 $vectorSearch filter 특정 제한 사항 을 제외하고 다음 MQL 일치 표현식에 대해 옵션을 지원합니다.

$vectorSearch filter 옵션은 다음 집계 파이프라인 연산자만 지원합니다.

참고

$vectorSearch filter 옵션은 다른 쿼리집계 파이프라인 연산자를 지원하지 않습니다.

$vectorSearch 다음 MongoDB 버전을 실행하는 Atlas cluster에서만 지원됩니다.

  • v6.0.11

  • v7.0.2 이상(RC 포함)

objectId 유형이 있는 필드의 옵션은 $vectorSearch filter 다음 MQL 일치 표현식을 지원하지 않습니다.

$vectorSearch보기 정의 및 다음 파이프라인 단계에서 사용할 수 없습니다.

$vectorSearch 의 결과를 이 단계로 전달할 수 있습니다.

Atlas , mongosh 및 다음 $vectorSearch드라이버를 사용하여 쿼리를 실행할 수 있습니다.Data Explorer

Atlas CLI를 사용하여 만든 로컬 Atlas 배포와 함께 Atlas Vector Search를 사용할 수도 있습니다. 자세한 내용은 로컬 Atlas 배포 만들기를 참조하세요.

벡터 검색 쿼리 처리를 격리하려면 전용 검색 노드 를 사용하는 것이 좋습니다. 전용 검색 노드에서 쿼리 성능이 향상될 수 있습니다. CPU가 높은 시스템은 더 많은 성능 향상을 제공할 수 있습니다. Atlas Vector Search가 검색 노드에서 실행되면 Atlas Vector Search는 데이터 세그먼트에 걸쳐 쿼리 실행을 병렬화합니다.

쿼리 처리를 병렬화하면 대규모 데이터 세트의 쿼리처럼 대부분의 경우 응답 시간이 개선됩니다. Atlas Vector Search 쿼리 처리 도중 쿼리 내 병렬 처리를 사용하면 더 많은 리소스를 사용하면서도 각각의 개별적인 쿼리의 지연 시간을 개선할 수 있습니다.

참고

Atlas Vector Search는 각 쿼리가 동시에 실행된다고 보장하지 않습니다. 예를 들어 대기 중인 동시 쿼리가 너무 많으면 Atlas Vector Search가 단일 스레드 실행으로 대체될 수 있습니다.

다음 쿼리는 $vectorSearch 단계를 사용하여 샘플 sample_mflix.embedded_movies 컬렉션을 검색합니다. 쿼리는 OpenAI의 text-embedding-ada-002 임베딩 모델을 사용하여 생성된 임베딩이 포함된 plot_embedding 필드를 검색합니다. 샘플 컬렉션 을 Atlas 클러스터에 추가하고 컬렉션에 대한 샘플 인덱스 를 만든 경우, 컬렉션에 대해 다음 쿼리를 실행할 수 있습니다.

참고

샘플 코드의 queryVector 를 터미널에 붙여넣는 데는 시스템에 따라 시간이 걸릴 수 있습니다.


언어 선택 드롭다운 메뉴를 사용하여 이 페이지에 있는 예제의 언어를 설정합니다.


다음 쿼리는 $vectorSearch 단계를 사용하여 문자열 time 여행 에 대한 벡터 임베딩을 사용하여 plot_embedding 필드를 검색합니다. 가장 가까운 이웃을 최대 150 고려하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 plottitle 필드만 결과에 포함합니다.

  • 결과에서 각 문서에 대한 벡터 검색 점수 를 표시하는 score 필드를 추가합니다.

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding",
6 "queryVector": 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1[
2 {
3 plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
4 title: 'Thrill Seekers',
5 score: 0.7892671227455139
6 },
7 {
8 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.',
9 title: 'About Time',
10 score: 0.7843604683876038
11 },
12 {
13 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.',
14 title: 'The Time Machine',
15 score: 0.7801066637039185
16 },
17 {
18 plot: "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
19 title: 'Crusade in Jeans',
20 score: 0.7789170742034912
21 },
22 {
23 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.',
24 title: 'Timecop',
25 score: 0.7771612405776978
26 },
27 {
28 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...',
29 title: 'A.P.E.X.',
30 score: 0.7730885744094849
31 },
32 {
33 plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
34 title: 'Men in Black 3',
35 score: 0.7712380886077881
36 },
37 {
38 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.',
39 title: 'Tomorrowland',
40 score: 0.7669923901557922
41 },
42 {
43 plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
44 title: 'Love Story 2050',
45 score: 0.7649372816085815
46 },
47 {
48 plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...',
49 title: 'The Portal',
50 score: 0.7640786170959473
51 }
52]

다음 쿼리는 01 1955 ~ 01 1975 사이에 개봉한 영화의 문서를 필터링한 후 샘플 벡터 데이터에 대해 시맨틱 Atlas Search를 수행합니다. $and 연산자를 사용하여 지정된 날짜에 대한 논리적 AND 연산을 수행합니다. 그런 다음 필터링된 문서의 plot_embedding 필드에서 string 어린이 모험 에 대한 벡터 임베딩을 사용하여 150 가장 가까운 이웃을 검색하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 결과에 plot, titleyear 필드만 포함합니다.

  • 결과에서 문서의 벡터 Atlas Search 점수를 표시하는 score 필드를 추가합니다.

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector-search-tutorial",
5 "path": "plot_embedding",
6 "filter": {
7 "$and": [
8 {
9 "year": { "$gt": 1955 }
10 },
11 {
12 "year": { "$lt": 1975 }
13 }
14 ]
15 },
16 "queryVector": 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21 {
22 "$project": {
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1[
2 {
3 plot: 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...',
4 title: 'Peter Pan',
5 year: 1960,
6 score: 0.748110830783844
7 },
8 {
9 plot: 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.',
10 title: 'Chitty Chitty Bang Bang',
11 year: 1968,
12 score: 0.7442465424537659
13 },
14 {
15 plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
16 title: 'That Man from Rio',
17 year: 1964,
18 score: 0.7416020035743713
19 },
20 {
21 plot: 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.',
22 title: 'The Little Prince',
23 year: 1974,
24 score: 0.7378944158554077
25 },
26 {
27 plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.',
28 title: 'The Red Balloon',
29 year: 1956,
30 score: 0.7342712879180908
31 },
32 {
33 plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.',
34 title: 'Willy Wonka & the Chocolate Factory',
35 year: 1971,
36 score: 0.7342107892036438
37 },
38 {
39 plot: 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.',
40 title: 'Bedknobs and Broomsticks',
41 year: 1971,
42 score: 0.7339356541633606
43 },
44 {
45 plot: "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.",
46 title: 'Pastoral Hide and Seek',
47 year: 1974,
48 score: 0.733299970626831
49 },
50 {
51 plot: 'A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.',
52 title: 'The Three Musketeers',
53 year: 1973,
54 score: 0.7331198453903198
55 },
56 {
57 plot: 'A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.',
58 title: 'Frosty',
59 year: 1964,
60 score: 0.7318308353424072
61 }
62]

Atlas Vector Search는 ~ year 범위의 필드 값을 기준으로 문서를 1955 1975필터링합니다. 부터 사이에 개봉된 영화의 줄거리에 나오는 어린이들의 모험을 요약한 문서를 1955 1975반환합니다.

추가 필터 예시도 참조하세요.

Atlas Cluster의 데이터에 대해 시맨틱 검색을 수행하는 방법 튜토리얼에서는 sample_mflix.embedded_movies collection에 포함된 데이터에 대해 시맨틱 검색 쿼리의 다른 사전 필터를 보여줍니다.

다음 쿼리는 $vectorSearch 단계를 사용하여 문자열 time 여행 에 대한 벡터 임베딩을 사용하여 plot_embedding 필드를 검색합니다. 가장 가까운 이웃을 최대 150 고려하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 plottitle 필드만 결과에 포함합니다.

  • 결과에서 각 문서에 대한 벡터 검색 점수 를 표시하는 score 필드를 추가합니다.

1using MongoDB.Bson;
2using MongoDB.Bson.Serialization.Attributes;
3using MongoDB.Bson.Serialization.Conventions;
4using MongoDB.Driver;
5using MongoDB.Driver.Search;
6
7public class vectorSearchBasicQuery
8{
9 // define connection to your Atlas cluster
10 private const string MongoConnectionString = "<connection-string>";
11
12 public static void Main(string[] args){
13 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
14 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
15
16 // connect to your Atlas cluster
17 var mongoClient = new MongoClient(MongoConnectionString);
18
19 // define namespace
20 var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
21 var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
22
23 // define vector embeddings to search
24 var vector = new[] 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25 var options = new VectorSearchOptions<EmbeddedMovie>() {
26 IndexName = "vector_index",
27 NumberOfCandidates = 150
28 };
29
30 // run query
31 var results = moviesCollection.Aggregate()
32 .VectorSearch(m => m.Embedding, vector, 10, options)
33 .Project(Builders<EmbeddedMovie>.Projection
34 .Include(m => m.Title)
35 .Include(m => m.Plot)
36 .MetaVectorSearchScore(m => m.Score))
37 .ToList();
38
39 // print results
40 foreach (var movie in results)
41 {
42 Console.WriteLine(movie.ToJson());
43 }
44 }
45}
46
47[BsonIgnoreExtraElements]
48public class EmbeddedMovie
49{
50 [BsonIgnoreIfDefault]
51 public string Title { get; set; }
52 public string Plot { get; set; }
53 [BsonElement("plot_embedding")]
54 public double[] Embedding { get; set; }
55 public double Score { get; set; }
56}
1{ "_id" : ObjectId("573a13a0f29313caabd04a4f"), "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", "score" : 0.78926712274551392 }
2{ "_id" : ObjectId("573a13d8f29313caabda6557"), "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", "score" : 0.78436046838760376 }
3{ "_id" : ObjectId("573a13a5f29313caabd13b4b"), "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", "score" : 0.78010666370391846 }
4{ "_id" : ObjectId("573a13aef29313caabd2e2d7"), "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : 0.77891707420349121 }
5{ "_id" : ObjectId("573a1399f29313caabceec0e"), "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", "score" : 0.77716124057769775 }
6{ "_id" : ObjectId("573a1399f29313caabcee36f"), "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.", "score" : 0.77308857440948486 }
7{ "_id" : ObjectId("573a13c6f29313caabd715d3"), "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : 0.77123808860778809 }
8{ "_id" : ObjectId("573a13d4f29313caabd98c13"), "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.", "title" : "Tomorrowland", "score" : 0.76699239015579224 }
9{ "_id" : ObjectId("573a13b6f29313caabd477fa"), "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : 0.76493728160858154 }
10{ "_id" : ObjectId("573a13e5f29313caabdc40c9"), "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal", "score" : 0.76407861709594727 }

다음 쿼리는 01 1955 ~ 01 1975 사이에 개봉한 영화의 문서를 필터링한 후 샘플 벡터 데이터에 대해 시맨틱 Atlas Search를 수행합니다. $and 연산자를 사용하여 지정된 날짜에 대한 논리적 AND 연산을 수행합니다. 그런 다음 필터링된 문서의 plot_embedding 필드에서 string 어린이 모험 에 대한 벡터 임베딩을 사용하여 150 가장 가까운 이웃을 검색하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 결과에 plot, titleyear 필드만 포함합니다.

  • 결과에서 문서의 벡터 Atlas Search 점수를 표시하는 score 필드를 추가합니다.

1using System.Reflection.Emit;
2using MongoDB.Bson;
3using MongoDB.Bson.Serialization.Attributes;
4using MongoDB.Bson.Serialization.Conventions;
5using MongoDB.Driver;
6using MongoDB.Driver.Search;
7
8public class vectorSearchFilterQuery
9{
10 // define connection to your Atlas cluster
11 private const string MongoConnectionString = "<connection-string>";
12
13 public static void Main(string[] args){
14 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
15 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
16
17 // connect to your Atlas cluster
18 var mongoClient = new MongoClient(MongoConnectionString);
19
20 // define namespace
21 var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
22 var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
23
24 // define vector embeddings to search
25 var vector = new[] 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26 // define filter
27 var yearGtFilter = Builders<EmbeddedMovie>.Filter.Gt("year", 1955);
28 var yearLtFilter = Builders<EmbeddedMovie>.Filter.Lt("year", 1975);
29 // define options
30 var options = new VectorSearchOptions<EmbeddedMovie>() {
31 Filter = Builders<EmbeddedMovie>.Filter.And(yearGtFilter, yearLtFilter),
32 IndexName = "vector_index",
33 NumberOfCandidates = 150
34 };
35
36 // run query
37 var results = moviesCollection.Aggregate()
38 .VectorSearch(m => m.Embedding, vector, 10, options)
39 .Project(Builders<EmbeddedMovie>.Projection
40 .Include(m => m.Title)
41 .Include(movie => movie.Plot)
42 .Include(movie => movie.Year)
43 .MetaVectorSearchScore(m => m.Score))
44 .ToList();
45
46 // print results
47 foreach (var movie in results)
48 {
49 Console.WriteLine(movie.ToJson());
50 }
51 }
52}
53
54[BsonIgnoreExtraElements]
55public class EmbeddedMovie
56{
57 [BsonIgnoreIfDefault]
58 public string Title { get; set; }
59 public string Plot { get; set; }
60 public int Year { get; set; }
61 [BsonElement("plot_embedding")]
62 public double[] Embedding { get; set; }
63 public double Score { get; set; }
64}
1{ "_id" : ObjectId("573a1395f29313caabce143b"), "plot" : "In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...", "title" : "Peter Pan", "year" : 1960, "score" : 0.74811083078384399 }
2{ "_id" : ObjectId("573a1396f29313caabce34d3"), "plot" : "A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.", "title" : "Chitty Chitty Bang Bang", "year" : 1968, "score" : 0.74424654245376587 }
3{ "_id" : ObjectId("573a1395f29313caabce23cb"), "plot" : "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "title" : "That Man from Rio", "year" : 1964, "score" : 0.74160200357437134 }
4{ "_id" : ObjectId("573a1396f29313caabce5648"), "plot" : "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title" : "The Little Prince", "year" : 1974, "score" : 0.73789441585540771 }
5{ "_id" : ObjectId("573a1394f29313caabce000d"), "plot" : "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title" : "The Red Balloon", "year" : 1956, "score" : 0.73427128791809082 }
6{ "_id" : ObjectId("573a1396f29313caabce482f"), "plot" : "A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.", "title" : "Willy Wonka & the Chocolate Factory", "year" : 1971, "score" : 0.7342107892036438 }
7{ "_id" : ObjectId("573a1396f29313caabce43e1"), "plot" : "An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.", "title" : "Bedknobs and Broomsticks", "year" : 1971, "score" : 0.7339356541633606 }
8{ "_id" : ObjectId("573a1396f29313caabce54e6"), "plot" : "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", "title" : "Pastoral Hide and Seek", "year" : 1974, "score" : 0.73329997062683105 }
9{ "_id" : ObjectId("573a1396f29313caabce582d"), "plot" : "A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.", "title" : "The Three Musketeers", "year" : 1973, "score" : 0.73311984539031982 }
10{ "_id" : ObjectId("573a1395f29313caabce243e"), "plot" : "A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.", "title" : "Frosty", "year" : 1964, "score" : 0.73183083534240723 }

Atlas Vector Search는 ~ year 범위의 필드 값을 기준으로 문서를 1955 1975필터링합니다. 부터 사이에 개봉된 영화의 줄거리에 나오는 어린이들의 모험을 요약한 문서를 1955 1975반환합니다.

추가 필터 예시도 참조하세요.

Atlas Cluster의 데이터에 대해 시맨틱 검색을 수행하는 방법 튜토리얼에서는 sample_mflix.embedded_movies collection에 포함된 데이터에 대해 시맨틱 검색 쿼리의 다른 사전 필터를 보여줍니다.

다음 쿼리는 $vectorSearch 단계를 사용하여 문자열 time 여행 에 대한 벡터 임베딩을 사용하여 plot_embedding 필드를 검색합니다. 가장 가까운 이웃을 최대 150 고려하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 plottitle 필드만 결과에 포함합니다.

  • 결과에서 각 문서에 대한 벡터 검색 점수 를 표시하는 score 필드를 추가합니다.

1import static com.mongodb.client.model.Aggregates.*;
2import static com.mongodb.client.model.Projections.*;
3import static com.mongodb.client.model.search.SearchPath.fieldPath;
4import static java.util.Arrays.asList;
5import com.mongodb.client.MongoClient;
6import com.mongodb.client.MongoClients;
7import com.mongodb.client.MongoCollection;
8import com.mongodb.client.MongoDatabase;
9import java.util.List;
10import com.mongodb.client.model.search.FieldSearchPath;
11import org.bson.Document;
12import org.bson.conversions.Bson;
13
14public class BasicQuery {
15 public static void main( String[] args ) {
16 // specify connection
17 String uri = "<connection-string>";
18
19 // establish connection and set namespace
20 try (MongoClient mongoClient = MongoClients.create(uri)) {
21 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
22 MongoCollection<Document> collection = database.getCollection("embedded_movies");
23
24 // define $vectorSearch query options
25 List<Double> queryVector = (asList(-0.0016261312d, -0.028070757d, -0.011342932d, -0.012775794d, -0.0027440966d, 0.008683807d, -0.02575152d, -0.02020668d, -0.010283281d, -0.0041719596d, 0.021392956d, 0.028657231d, -0.006634482d, 0.007490867d, 0.018593878d, 0.0038187427d, 0.029590257d, -0.01451522d, 0.016061379d, 0.00008528442d, -0.008943722d, 0.01627464d, 0.024311995d, -0.025911469d, 0.00022596726d, -0.008863748d, 0.008823762d, -0.034921836d, 0.007910728d, -0.01515501d, 0.035801545d, -0.0035688248d, -0.020299982d, -0.03145631d, -0.032256044d, -0.028763862d, -0.0071576433d, -0.012769129d, 0.012322609d, -0.006621153d, 0.010583182d, 0.024085402d, -0.001623632d, 0.007864078d, -0.021406285d, 0.002554159d, 0.012229307d, -0.011762793d, 0.0051682983d, 0.0048484034d, 0.018087378d, 0.024325324d, -0.037694257d, -0.026537929d, -0.008803768d, -0.017767483d, -0.012642504d, -0.0062712682d, 0.0009771782d, -0.010409906d, 0.017754154d, -0.004671795d, -0.030469967d, 0.008477209d, -0.005218282d, -0.0058480743d, -0.020153364d, -0.0032805866d, 0.004248601d, 0.0051449724d, 0.006791097d, 0.007650814d, 0.003458861d, -0.0031223053d, -0.01932697d, -0.033615597d, 0.00745088d, 0.006321252d, -0.0038154104d, 0.014555207d, 0.027697546d, -0.02828402d, 0.0066711367d, 0.0077107945d, 0.01794076d, 0.011349596d, -0.0052715978d, 0.014755142d, -0.019753495d, -0.011156326d, 0.011202978d, 0.022126047d, 0.00846388d, 0.030549942d, -0.0041386373d, 0.018847128d, -0.00033655585d, 0.024925126d, -0.003555496d, -0.019300312d, 0.010749794d, 0.0075308536d, -0.018287312d, -0.016567878d, -0.012869096d, -0.015528221d, 0.0078107617d, -0.011156326d, 0.013522214d, -0.020646535d, -0.01211601d, 0.055928253d, 0.011596181d, -0.017247654d, 0.0005939711d, -0.026977783d, -0.003942035d, -0.009583511d, -0.0055248477d, -0.028737204d, 0.023179034d, 0.003995351d, 0.0219661d, -0.008470545d, 0.023392297d, 0.010469886d, -0.015874773d, 0.007890735d, -0.009690142d, -0.00024970944d, 0.012775794d, 0.0114762215d, 0.013422247d, 0.010429899d, -0.03686786d, -0.006717788d, -0.027484283d, 0.011556195d, -0.036068123d, -0.013915418d, -0.0016327957d, 0.0151016945d, -0.020473259d, 0.004671795d, -0.012555866d, 0.0209531d, 0.01982014d, 0.024485271d, 0.0105431955d, -0.005178295d, 0.033162415d, -0.013795458d, 0.007150979d, 0.010243294d, 0.005644808d, 0.017260984d, -0.0045618312d, 0.0024725192d, 0.004305249d, -0.008197301d, 0.0014203656d, 0.0018460588d, 0.005015015d, -0.011142998d, 0.01439526d, 0.022965772d, 0.02552493d, 0.007757446d, -0.0019726837d, 0.009503538d, -0.032042783d, 0.008403899d, -0.04609149d, 0.013808787d, 0.011749465d, 0.036388017d, 0.016314628d, 0.021939443d, -0.0250051d, -0.017354285d, -0.012962398d, 0.00006107364d, 0.019113706d, 0.03081652d, -0.018114036d, -0.0084572155d, 0.009643491d, -0.0034721901d, 0.0072642746d, -0.0090636825d, 0.01642126d, 0.013428912d, 0.027724205d, 0.0071243206d, -0.6858542d, -0.031029783d, -0.014595194d, -0.011449563d, 0.017514233d, 0.01743426d, 0.009950057d, 0.0029706885d, -0.015714826d, -0.001806072d, 0.011856096d, 0.026444625d, -0.0010663156d, -0.006474535d, 0.0016161345d, -0.020313311d, 0.0148351155d, -0.0018393943d, 0.0057347785d, 0.018300641d, -0.018647194d, 0.03345565d, -0.008070676d, 0.0071443142d, 0.014301958d, 0.0044818576d, 0.003838736d, -0.007350913d, -0.024525259d, -0.001142124d, -0.018620536d, 0.017247654d, 0.007037683d, 0.010236629d, 0.06046009d, 0.0138887605d, -0.012122675d, 0.037694257d, 0.0055081863d, 0.042492677d, 0.00021784494d, -0.011656162d, 0.010276617d, 0.022325981d, 0.005984696d, -0.009496873d, 0.013382261d, -0.0010563189d, 0.0026507939d, -0.041639622d, 0.008637156d, 0.026471283d, -0.008403899d, 0.024858482d, -0.00066686375d, -0.0016252982d, 0.027590916d, 0.0051449724d, 0.0058647357d, -0.008743787d, -0.014968405d, 0.027724205d, -0.011596181d, 0.0047650975d, -0.015381602d, 0.0043718936d, 0.002159289d, 0.035908177d, -0.008243952d, -0.030443309d, 0.027564257d, 0.042625964d, -0.0033688906d, 0.01843393d, 0.019087048d, 0.024578573d, 0.03268257d, -0.015608194d, -0.014128681d, -0.0033538956d, -0.0028757197d, -0.004121976d, -0.032389335d, 0.0034322033d, 0.058807302d, 0.010943064d, -0.030523283d, 0.008903735d, 0.017500903d, 0.00871713d, -0.0029406983d, 0.013995391d, -0.03132302d, -0.019660193d, -0.00770413d, -0.0038853872d, 0.0015894766d, -0.0015294964d, -0.006251275d, -0.021099718d, -0.010256623d, -0.008863748d, 0.028550599d, 0.02020668d, -0.0012962399d, -0.003415542d, -0.0022509254d, 0.0119360695d, 0.027590916d, -0.046971202d, -0.0015194997d, -0.022405956d, 0.0016677842d, -0.00018535563d, -0.015421589d, -0.031802863d, 0.03814744d, 0.0065411795d, 0.016567878d, -0.015621523d, 0.022899127d, -0.011076353d, 0.02841731d, -0.002679118d, -0.002342562d, 0.015341615d, 0.01804739d, -0.020566562d, -0.012989056d, -0.002990682d, 0.01643459d, 0.00042527664d, 0.008243952d, -0.013715484d, -0.004835075d, -0.009803439d, 0.03129636d, -0.021432944d, 0.0012087687d, -0.015741484d, -0.0052016205d, 0.00080890034d, -0.01755422d, 0.004811749d, -0.017967418d, -0.026684547d, -0.014128681d, 0.0041386373d, -0.013742141d, -0.010056688d, -0.013268964d, -0.0110630235d, -0.028337335d, 0.015981404d, -0.00997005d, -0.02424535d, -0.013968734d, -0.028310679d, -0.027750863d, -0.020699851d, 0.02235264d, 0.001057985d, 0.00081639783d, -0.0099367285d, 0.013522214d, -0.012016043d, -0.00086471526d, 0.013568865d, 0.0019376953d, -0.019020405d, 0.017460918d, -0.023045745d, 0.008503866d, 0.0064678704d, -0.011509543d, 0.018727167d, -0.003372223d, -0.0028690554d, -0.0027024434d, -0.011902748d, -0.012182655d, -0.015714826d, -0.0098634185d, 0.00593138d, 0.018753825d, 0.0010146659d, 0.013029044d, 0.0003521757d, -0.017620865d, 0.04102649d, 0.00552818d, 0.024485271d, -0.009630162d, -0.015608194d, 0.0006718621d, -0.0008418062d, 0.012395918d, 0.0057980907d, 0.016221326d, 0.010616505d, 0.004838407d, -0.012402583d, 0.019900113d, -0.0034521967d, 0.000247002d, -0.03153628d, 0.0011038032d, -0.020819811d, 0.016234655d, -0.00330058d, -0.0032289368d, 0.00078973995d, -0.021952773d, -0.022459272d, 0.03118973d, 0.03673457d, -0.021472929d, 0.0072109587d, -0.015075036d, 0.004855068d, -0.0008151483d, 0.0069643734d, 0.010023367d, -0.010276617d, -0.023019087d, 0.0068244194d, -0.0012520878d, -0.0015086699d, 0.022046074d, -0.034148756d, -0.0022192693d, 0.002427534d, -0.0027124402d, 0.0060346797d, 0.015461575d, 0.0137554705d, 0.009230294d, -0.009583511d, 0.032629255d, 0.015994733d, -0.019167023d, -0.009203636d, 0.03393549d, -0.017274313d, -0.012042701d, -0.0009930064d, 0.026777849d, -0.013582194d, -0.0027590916d, -0.017594207d, -0.026804507d, -0.0014236979d, -0.022032745d, 0.0091236625d, -0.0042419364d, -0.00858384d, -0.0033905501d, -0.020739838d, 0.016821127d, 0.022539245d, 0.015381602d, 0.015141681d, 0.028817179d, -0.019726837d, -0.0051283115d, -0.011489551d, -0.013208984d, -0.0047017853d, -0.0072309524d, 0.01767418d, 0.0025658219d, -0.010323267d, 0.012609182d, -0.028097415d, 0.026871152d, -0.010276617d, 0.021912785d, 0.0022542577d, 0.005124979d, -0.0019710176d, 0.004518512d, -0.040360045d, 0.010969722d, -0.0031539614d, -0.020366628d, -0.025778178d, -0.0110030435d, -0.016221326d, 0.0036587953d, 0.016207997d, 0.003007343d, -0.0032555948d, 0.0044052163d, -0.022046074d, -0.0008822095d, -0.009363583d, 0.028230704d, -0.024538586d, 0.0029840174d, 0.0016044717d, -0.014181997d, 0.031349678d, -0.014381931d, -0.027750863d, 0.02613806d, 0.0004136138d, -0.005748107d, -0.01868718d, -0.0010138329d, 0.0054348772d, 0.010703143d, -0.003682121d, 0.0030856507d, -0.004275259d, -0.010403241d, 0.021113047d, -0.022685863d, -0.023032416d, 0.031429652d, 0.001792743d, -0.005644808d, -0.011842767d, -0.04078657d, -0.0026874484d, 0.06915057d, -0.00056939584d, -0.013995391d, 0.010703143d, -0.013728813d, -0.022939114d, -0.015261642d, -0.022485929d, 0.016807798d, 0.007964044d, 0.0144219175d, 0.016821127d, 0.0076241563d, 0.005461535d, -0.013248971d, 0.015301628d, 0.0085171955d, -0.004318578d, 0.011136333d, -0.0059047225d, -0.010249958d, -0.018207338d, 0.024645219d, 0.021752838d, 0.0007614159d, -0.013648839d, 0.01111634d, -0.010503208d, -0.0038487327d, -0.008203966d, -0.00397869d, 0.0029740208d, 0.008530525d, 0.005261601d, 0.01642126d, -0.0038753906d, -0.013222313d, 0.026537929d, 0.024671877d, -0.043505676d, 0.014195326d, 0.024778508d, 0.0056914594d, -0.025951454d, 0.017620865d, -0.0021359634d, 0.008643821d, 0.021299653d, 0.0041686273d, -0.009017031d, 0.04044002d, 0.024378639d, -0.027777521d, -0.014208655d, 0.0028623908d, 0.042119466d, 0.005801423d, -0.028124074d, -0.03129636d, 0.022139376d, -0.022179363d, -0.04067994d, 0.013688826d, 0.013328944d, 0.0046184794d, -0.02828402d, -0.0063412455d, -0.0046184794d, -0.011756129d, -0.010383247d, -0.0018543894d, -0.0018593877d, -0.00052024535d, 0.004815081d, 0.014781799d, 0.018007403d, 0.01306903d, -0.020433271d, 0.009043689d, 0.033189073d, -0.006844413d, -0.019766824d, -0.018767154d, 0.00533491d, -0.0024575242d, 0.018727167d, 0.0058080875d, -0.013835444d, 0.0040719924d, 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26 String indexName = "vector_index";
27 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
28 int numCandidates = 150;
29 int limit = 10;
30
31 // define pipeline
32 List<Bson> pipeline = asList(
33 vectorSearch(
34 fieldSearchPath,
35 queryVector,
36 indexName,
37 numCandidates,
38 limit),
39 project(
40 fields(exclude("_id"), include("title"), include("plot"),
41 metaVectorSearchScore("score"))));
42
43 // run query and print results
44 collection.aggregate(pipeline)
45 .forEach(doc -> System.out.println(doc.toJson()));
46 }
47 }
48}
1{"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", "score": 0.7892671227455139}
2{"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", "score": 0.7843604683876038}
3{"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", "score": 0.7801066637039185}
4{"plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title": "Crusade in Jeans", "vectorSearchScore": 0.7789170742034912}
5{"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", "score": 0.7771612405776978}
6{"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.", "vectorSearchScore": 0.7730885744094849}
7{"plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title": "Men in Black 3", "score": 0.7712380886077881}
8{"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.", "title": "Tomorrowland", "score": 0.7669923901557922}
9{"plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title": "Love Story 2050", "vectorSearchScore": 0.7649372816085815}
10{"plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title": "The Portal", "score": 0.7640786170959473}

다음 쿼리는 01 1955 ~ 01 1975 사이에 개봉한 영화의 문서를 필터링한 후 샘플 벡터 데이터에 대해 시맨틱 Atlas Search를 수행합니다. $and 연산자를 사용하여 지정된 날짜에 대한 논리적 AND 연산을 수행합니다. 그런 다음 필터링된 문서의 plot_embedding 필드에서 string 어린이 모험 에 대한 벡터 임베딩을 사용하여 150 가장 가까운 이웃을 검색하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 결과에 plot, titleyear 필드만 포함합니다.

  • 결과에서 문서의 벡터 Atlas Search 점수를 표시하는 score 필드를 추가합니다.

1import static com.mongodb.client.model.Aggregates.*;
2import static com.mongodb.client.model.Projections.*;
3import static com.mongodb.client.model.search.SearchPath.fieldPath;
4import static com.mongodb.client.model.search.VectorSearchOptions.vectorSearchOptions;
5import static java.util.Arrays.asList;
6
7import com.mongodb.client.MongoClient;
8import com.mongodb.client.MongoClients;
9import com.mongodb.client.MongoCollection;
10import com.mongodb.client.MongoDatabase;
11import com.mongodb.client.model.*;
12import com.mongodb.client.model.search.VectorSearchOptions;
13import java.util.List;
14import com.mongodb.client.model.search.FieldSearchPath;
15import org.bson.Document;
16import org.bson.conversions.Bson;
17
18public class FilterQuery {
19 public static void main( String[] args ) {
20 // specify connection
21 String uri = "<connection-string>";
22
23 // establish connection and set namespace
24 try (MongoClient mongoClient = MongoClients.create(uri)) {
25 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
26 MongoCollection<Document> collection = database.getCollection("embedded_movies");
27
28 // define $vectorSearch query options
29 List<Double> queryVector = 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30 String indexName = "vector_index";
31 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
32 int numCandidates = 150;
33 int limit = 10;
34 Bson criteria = Filters.and(Filters.gt("year", 1955), Filters.lt("year", 1975));
35 VectorSearchOptions options = vectorSearchOptions().filter(criteria);
36
37 // define pipeline
38 List<Bson> pipeline = asList(
39 vectorSearch(
40 fieldSearchPath,
41 queryVector,
42 indexName,
43 numCandidates,
44 limit,
45 options),
46 project(
47 fields(exclude("_id"), include("title"), include("plot"), include("year"),
48 metaVectorSearchScore("score"))));
49
50 // run query and print results
51 collection.aggregate(pipeline)
52 .forEach(doc -> System.out.println(doc.toJson()));
53 }
54 }
55}
1{"plot": "In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...", "title": "Peter Pan", "year": 1960, "score": 0.748110830783844}
2{"plot": "A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.", "title": "Chitty Chitty Bang Bang", "year": 1968, "score": 0.7442465424537659}
3{"plot": "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "title": "That Man from Rio", "year": 1964, "score": 0.7416020035743713}
4{"plot": "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title": "The Little Prince", "year": 1974, "score": 0.7378944158554077}
5{"plot": "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title": "The Red Balloon", "year": 1956, "score": 0.7342712879180908}
6{"plot": "A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.", "title": "Willy Wonka & the Chocolate Factory", "year": 1971, "score": 0.7342107892036438}
7{"plot": "An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.", "title": "Bedknobs and Broomsticks", "year": 1971, "score": 0.7339356541633606}
8{"plot": "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", "title": "Pastoral Hide and Seek", "year": 1974, "score": 0.733299970626831}
9{"plot": "A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.", "title": "The Three Musketeers", "year": 1973, "score": 0.7331198453903198}
10{"plot": "A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.", "title": "Frosty", "year": 1964, "score": 0.7318308353424072}

Atlas Vector Search는 ~ year 범위의 필드 값을 기준으로 문서를 1955 1975필터링합니다. 부터 사이에 개봉된 영화의 줄거리에 나오는 어린이들의 모험을 요약한 문서를 1955 1975반환합니다.

추가 필터 예시도 참조하세요.

Atlas Cluster의 데이터에 대해 시맨틱 검색을 수행하는 방법 튜토리얼에서는 sample_mflix.embedded_movies collection에 포함된 데이터에 대해 시맨틱 검색 쿼리의 다른 사전 필터를 보여줍니다.

다음 쿼리는 $vectorSearch 단계를 사용하여 문자열 time 여행 에 대한 벡터 임베딩을 사용하여 plot_embedding 필드를 검색합니다. 가장 가까운 이웃을 최대 150 고려하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 plottitle 필드만 결과에 포함합니다.

  • 결과에서 각 문서에 대한 벡터 검색 점수 를 표시하는 score 필드를 추가합니다.

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async function run() {
9 try {
10 await client.connect();
11
12 // set namespace
13 const database = client.db("sample_mflix");
14 const coll = database.collection("embedded_movies");
15
16 // define pipeline
17 const agg = [
18 {
19 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding',
22 'queryVector': [
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24 ],
25 'numCandidates': 150,
26 'limit': 10
27 }
28 }, {
29 '$project': {
30 '_id': 0,
31 'plot': 1,
32 'title': 1,
33 'score': {
34 '$meta': 'vectorSearchScore'
35 }
36 }
37 }
38 ];
39 // run pipeline
40 const result = coll.aggregate(agg);
41
42 // print results
43 await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44 } finally {
45 await client.close();
46 }
47}
48run().catch(console.dir);
1'{"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","score":0.7892671227455139}'
2'{"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","score":0.7843604683876038}'
3'{"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","score":0.7801066637039185}'
4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5'{"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","score":0.7771612405776978}'
6'{"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.","score":0.7730885744094849}'
7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8'{"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.","title":"Tomorrowland","score":0.7669923901557922}'
9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'

다음 쿼리는 01 1955 ~ 01 1975 사이에 개봉한 영화의 문서를 필터링한 후 샘플 벡터 데이터에 대해 시맨틱 Atlas Search를 수행합니다. $and 연산자를 사용하여 지정된 날짜에 대한 논리적 AND 연산을 수행합니다. 그런 다음 필터링된 문서의 plot_embedding 필드에서 string 어린이 모험 에 대한 벡터 임베딩을 사용하여 150 가장 가까운 이웃을 검색하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 결과에 plot, titleyear 필드만 포함합니다.

  • 결과에서 문서의 벡터 Atlas Search 점수를 표시하는 score 필드를 추가합니다.

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async function run() {
9 try {
10 await client.connect();
11
12 // set namespace
13 const database = client.db("sample_mflix");
14 const coll = database.collection("embedded_movies");
15
16 // define pipeline
17 const agg = [
18 {
19 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding',
22 'filter': {
23 '$and': [
24 {
25 'year': {
26 '$gt': 1955
27 },
28 'year': {
29 '$lt': 1975
30 }
31 }
32 ]
33 },
34 'queryVector': [0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 0.010624337, -0.024236975, -0.0048659067, 0.015153068, -0.000490888, -0.022161031, -0.0024560927, -0.007411252, 0.009745035, -0.01886861, 0.02112967, -0.011939983, 0.015153068, 0.0025800543, 0.017824028, -0.02410475, -0.016633997, -0.0018214093, -0.008323609, -0.009222744, 0.009388026, -0.0028296304, 0.0017536436, 0.0065517845, -0.011635863, -0.028454976, -0.018934723, 0.012951509, -0.0032015154, -0.005880739, -0.03115238, 0.012951509, 0.0057749585, 0.009202911, -0.0069352393, 0.00205611, 0.0063732797, 0.0039700773, -0.007100521, -0.0077087595, 0.011596196, -0.010207825, -0.007100521, -0.0051006074, -0.01670011, 0.012773004, -0.035304267, -0.0074971984, 0.0025800543, -0.006118745, 0.030253245, -0.0010751605, 0.039456155, 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0.032871313, 0.011549917, 0.010928456, -0.014253933, -0.011384634, 0.00894507, 0.034616694, -0.016872002, -0.010987958, -0.011953205],
35 'numCandidates': 150,
36 'limit': 10
37 }
38 }, {
39 '$project': {
40 '_id': 0,
41 'title': 1,
42 'plot': 1,
43 'year': 1,
44 'score': {
45 '$meta': 'vectorSearchScore'
46 }
47 }
48 }
49 ];
50 // run pipeline
51 const result = coll.aggregate(agg);
52
53 // print results
54 await result.forEach((doc) => console.dir(JSON.stringify(doc)));
55 } finally {
56 await client.close();
57 }
58}
59run().catch(console.dir);
1'{"plot":"In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...","title":"Peter Pan","year":1960,"score":0.748110830783844}'
2'{"plot":"A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.","title":"Chitty Chitty Bang Bang","year":1968,"score":0.7442465424537659}'
3'{"plot":"A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.","title":"That Man from Rio","year":1964,"score":0.7416020035743713}'
4'{"plot":"A boy raised by wolves tries to adapt to human village life.","title":"Jungle Book","year":1942,"score":0.7387760877609253}'
5'{"plot":"A pilot, stranded in the desert, meets a little boy who is a prince on a planet.","title":"The Little Prince","year":1974,"score":0.7378944158554077}'
6'{"plot":"A red balloon with a life of its own follows a little boy around the streets of Paris.","title":"The Red Balloon","year":1956,"score":0.7342712879180908}'
7'{"plot":"A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.","title":"Willy Wonka & the Chocolate Factory","year":1971,"score":0.7342107892036438}'
8'{"plot":"An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.","title":"Bedknobs and Broomsticks","year":1971,"score":0.7339356541633606}'
9'{"plot":"Arriving home to find his native land under the yoke of corrupt merchants, an adventurer named Sadko sets sail in search of a mythical bird of happiness.","title":"Sadko","year":1953,"score":0.7339220643043518}'
10`{"plot":"A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.","title":"Pastoral Hide and Seek","year":1974,"score":0.733299970626831}`

Atlas Vector Search는 ~ year 범위의 필드 값을 기준으로 문서를 1955 1975필터링합니다. 부터 사이에 개봉된 영화의 줄거리에 나오는 어린이들의 모험을 요약한 문서를 1955 1975반환합니다.

추가 필터 예시도 참조하세요.

Atlas Cluster의 데이터에 대해 시맨틱 검색을 수행하는 방법 튜토리얼에서는 sample_mflix.embedded_movies collection에 포함된 데이터에 대해 시맨틱 검색 쿼리의 다른 사전 필터를 보여줍니다.

다음 쿼리는 $vectorSearch 단계를 사용하여 문자열 time 여행 에 대한 벡터 임베딩을 사용하여 plot_embedding 필드를 검색합니다. 가장 가까운 이웃을 최대 150 고려하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 plottitle 필드만 결과에 포함합니다.

  • 결과에서 각 문서에 대한 벡터 검색 점수 를 표시하는 score 필드를 추가합니다.

1import pymongo
2import datetime
3
4# connect to your Atlas cluster
5client = pymongo.MongoClient("<connection-string>")
6
7# define pipeline
8pipeline = [
9 {
10 '$vectorSearch': {
11 'index': 'vector_index',
12 'path': 'plot_embedding',
13 'queryVector': [-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, -0.020473259, 0.004671795, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136],
14 'numCandidates': 150,
15 'limit': 10
16 }
17 }, {
18 '$project': {
19 '_id': 0,
20 'plot': 1,
21 'title': 1,
22 'score': {
23 '$meta': 'vectorSearchScore'
24 }
25 }
26 }
27]
28
29# run pipeline
30result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
31
32# print results
33for i in result:
34 print(i)
35
1{'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', 'score': 0.7892671227455139}
2{'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', 'score': 0.7843604683876038}
3{'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', 'score': 0.7801066637039185}
4{'plot': "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", 'title': 'Crusade in Jeans', 'score': 0.7789170742034912}
5{'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', 'score': 0.7771612405776978}
6{'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.', 'score': 0.7730885744094849}
7{'plot': "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 'title': 'Men in Black 3', 'score': 0.7712380886077881}
8{'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.', 'title': 'Tomorrowland', 'score': 0.7669923901557922}
9{'plot': 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 'title': 'Love Story 2050', 'score': 0.7649372816085815}
10{'plot': 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 'title': 'The Portal', 'score': 0.7640786170959473}

다음 쿼리는 01 1955 ~ 01 1975 사이에 개봉한 영화의 문서를 필터링한 후 샘플 벡터 데이터에 대해 시맨틱 Atlas Search를 수행합니다. $and 연산자를 사용하여 지정된 날짜에 대한 논리적 AND 연산을 수행합니다. 그런 다음 필터링된 문서의 plot_embedding 필드에서 string 어린이 모험 에 대한 벡터 임베딩을 사용하여 150 가장 가까운 이웃을 검색하고 결과에 10 문서를 반환합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 결과에 plot, titleyear 필드만 포함합니다.

  • 결과에서 문서의 벡터 Atlas Search 점수를 표시하는 score 필드를 추가합니다.

1import pymongo
2import datetime
3
4# connect to your Atlas cluster
5client = pymongo.MongoClient("<connection-string>")
6
7# define pipeline
8pipeline = [
9 {
10 '$vectorSearch': {
11 'index': 'vector_index',
12 'path': 'plot_embedding',
13 'filter': {
14 '$and': [
15 {
16 'year': {
17 '$gt': 1955
18 },
19 'year': {
20 '$lt': 1975
21 }
22 }
23 ]
24 },
25 'queryVector': [0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 0.010624337, -0.024236975, -0.0048659067, 0.015153068, -0.000490888, -0.022161031, -0.0024560927, -0.007411252, 0.009745035, -0.01886861, 0.02112967, -0.011939983, 0.015153068, 0.0025800543, 0.017824028, -0.02410475, -0.016633997, -0.0018214093, -0.008323609, -0.009222744, 0.009388026, -0.0028296304, 0.0017536436, 0.0065517845, -0.011635863, -0.028454976, -0.018934723, 0.012951509, -0.0032015154, -0.005880739, -0.03115238, 0.012951509, 0.0057749585, 0.009202911, -0.0069352393, 0.00205611, 0.0063732797, 0.0039700773, -0.007100521, -0.0077087595, 0.011596196, -0.010207825, -0.007100521, -0.0051006074, -0.01670011, 0.012773004, -0.035304267, -0.0074971984, 0.0025800543, -0.006118745, 0.030253245, -0.0010751605, 0.039456155, 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26 'numCandidates': 150,
27 'limit': 10
28 }
29 }, {
30 '$project': {
31 '_id': 0,
32 'title': 1,
33 'plot': 1,
34 'year': 1,
35 'score': {
36 '$meta': 'vectorSearchScore'
37 }
38 }
39 }
40]
41
42# run pipeline
43result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
44
45# print results
46for i in result:
47 print(i)
48
1{'plot': 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...', 'title': 'Peter Pan', 'year': 1960, 'score': 0.748110830783844}
2{'plot': 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.', 'title': 'Chitty Chitty Bang Bang', 'year': 1968, 'score': 0.7442465424537659}
3{'plot': 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 'title': 'That Man from Rio', 'year': 1964, 'score': 0.7416020035743713}
4{'plot': 'A boy raised by wolves tries to adapt to human village life.', 'title': 'Jungle Book', 'year': 1942, 'score': 0.7387760877609253}
5{'plot': 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.', 'title': 'The Little Prince', 'year': 1974, 'score': 0.7378944158554077}
6{'plot': 'A red balloon with a life of its own follows a little boy around the streets of Paris.', 'title': 'The Red Balloon', 'year': 1956, 'score': 0.7342712879180908}
7{'plot': 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.', 'title': 'Willy Wonka & the Chocolate Factory', 'year': 1971, 'score': 0.7342107892036438}
8{'plot': 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.', 'title': 'Bedknobs and Broomsticks', 'year': 1971, 'score': 0.7339356541633606}
9{'plot': 'Arriving home to find his native land under the yoke of corrupt merchants, an adventurer named Sadko sets sail in search of a mythical bird of happiness.', 'title': 'Sadko', 'year': 1953, 'score': 0.7339220643043518}
10{'plot': "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", 'title': 'Pastoral Hide and Seek', 'year': 1974, 'score': 0.733299970626831}

Atlas Vector Search는 ~ year 범위의 필드 값을 기준으로 문서를 1955 1975필터링합니다. 부터 사이에 개봉된 영화의 줄거리에 나오는 어린이들의 모험을 요약한 문서를 1955 1975반환합니다.

추가 필터 예시도 참조하세요.

Atlas Cluster의 데이터에 대해 시맨틱 검색을 수행하는 방법 튜토리얼에서는 sample_mflix.embedded_movies collection에 포함된 데이터에 대해 시맨틱 검색 쿼리의 다른 사전 필터를 보여줍니다.

다음 쿼리는 $vectorSearch 단계를 사용하여 문자열 세계 대전 에 대한 벡터 임베딩을 사용하여 plot_embedding 필드를 검색합니다. 정확히 일치하는 항목을 요청하고 결과를 10 문서로 제한합니다. 이 쿼리는 또한 다음을 수행하는 $project 단계를 지정합니다.

  • _id 필드를 제외하고 결과에 plot, titleyear 필드만 포함합니다.

  • 결과에서 문서의 벡터 Atlas Search 점수를 표시하는 score 필드를 추가합니다.

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding",
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1[
2 {
3 plot: 'It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.',
4 title: 'Red Dawn',
5 score: 0.7700583338737488
6 },
7 {
8 plot: 'A dramatization of the World War II Battle of Iwo Jima.',
9 title: 'Sands of Iwo Jima',
10 score: 0.7581185102462769
11 },
12 {
13 plot: 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.',
14 title: 'White Tiger',
15 score: 0.750884473323822
16 },
17 {
18 plot: "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.",
19 title: 'P-51 Dragon Fighter',
20 score: 0.749922513961792
21 },
22 {
23 plot: 'A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.',
24 title: 'When Trumpets Fade',
25 score: 0.7498313188552856
26 },
27 {
28 plot: 'Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.',
29 title: 'Battletruck',
30 score: 0.7497193217277527
31 },
32 {
33 plot: 'It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....',
34 title: 'Robot Jox',
35 score: 0.7495121955871582
36 },
37 {
38 plot: 'During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.',
39 title: 'The Enemy Below',
40 score: 0.746050238609314
41 },
42 {
43 plot: 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.',
44 title: 'Saints and Soldiers',
45 score: 0.7435222864151001
46 },
47 {
48 plot: 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.',
49 title: 'Saints and Soldiers',
50 score: 0.743497371673584
51 }
52]
Not yet available.
Not yet available.
1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async function run() {
9 try {
10 await client.connect();
11
12 // set namespace
13 const database = client.db("sample_mflix");
14 const coll = database.collection("embedded_movies");
15
16 // define pipeline
17 const agg = [
18 {
19 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding',
22 'queryVector': [
23 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24 ],
25 'exact': true,
26 'limit': 10
27 }
28 }, {
29 '$project': {
30 '_id': 0,
31 'plot': 1,
32 'title': 1,
33 'score': {
34 '$meta': 'vectorSearchScore'
35 }
36 }
37 }
38 ];
39 // run pipeline
40 const result = coll.aggregate(agg);
41
42 // print results
43 await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44 } finally {
45 await client.close();
46 }
47}
48run().catch(console.dir);
1{
2 "plot":"It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.",
3 "title":"Red Dawn",
4 "score":0.7700583338737488
5}
6{
7 "plot":"A dramatization of the World War II Battle of Iwo Jima.",
8 "title":"Sands of Iwo Jima",
9 "score":0.7581185102462769
10}
11{
12 "plot":"Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.",
13 "title":"White Tiger",
14 "score":0.750884473323822
15}
16{
17 "plot":"As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.",
18 "title":"P-51 Dragon Fighter",
19 "score":0.749922513961792
20}
21{
22 "plot":"A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.",
23 "title":"When Trumpets Fade",
24 "score":0.7498313188552856
25}
26{
27 "plot":"Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.",
28 "title":"Battletruck",
29 "score":0.7497193217277527
30}
31{
32 "plot":"It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....",
33 "title":"Robot Jox",
34 "score":0.7495121955871582
35}
36{
37 "plot":"During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.",
38 "title":"The Enemy Below",
39 "score":0.746050238609314
40}
41{
42 "plot":"Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.",
43 "title":"Saints and Soldiers",
44 "score":0.7435222864151001
45}
46{
47 "plot":"Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.",
48 "title":"Saints and Soldiers",
49 "score":0.743497371673584
50}
1import pymongo
2import datetime
3
4# connect to your Atlas cluster
5client = pymongo.MongoClient("<connection-string>")
6
7# define pipeline
8pipeline = [
9 {
10 '$vectorSearch': {
11 'index': 'vector_index',
12 'path': 'plot_embedding',
13 'queryVector': 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14 'exact': True,
15 'limit': 10
16 }
17 }, {
18 '$project': {
19 '_id': 0,
20 'plot': 1,
21 'title': 1,
22 'score': {
23 '$meta': 'vectorSearchScore'
24 }
25 }
26 }
27]
28
29# run pipeline
30result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
31
32# print results
33for i in result:
34 print(i)
35
1{
2 'plot': 'It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.',
3 'title': 'Red Dawn',
4 'score': 0.7700583338737488
5}
6{
7 'plot': 'A dramatization of the World War II Battle of Iwo Jima.',
8 'title': 'Sands of Iwo Jima',
9 'score': 0.7581185102462769
10}
11{
12 'plot': 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.',
13 'title': 'White Tiger',
14 'score': 0.750884473323822
15}
16{
17 'plot': "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.",
18 'title': 'P-51 Dragon Fighter',
19 'score': 0.749922513961792
20}
21{
22 'plot': 'A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.',
23 'title': 'When Trumpets Fade',
24 'score': 0.7498313188552856
25}
26{
27 'plot': 'Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.',
28 'score': 0.7497193217277527
29}
30{
31 'plot': 'It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....',
32 'title': 'Robot Jox',
33 'score': 0.7495121955871582
34}
35{
36 'plot': 'During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.',
37 'title': 'The Enemy Below',
38 'score': 0.746050238609314}
39{'plot': 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 'title': 'Saints and Soldiers', 'score': 0.7435222864151001
40}
41{
42 'plot': 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.',
43 'title': 'Saints and Soldiers',
44 'score': 0.743497371673584
45}

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