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MongoDB Vector Search

개요

이 가이드 에서는 Java Reactive Streams 운전자 의 MongoDB Vector Search 기능 사용하는 방법을 학습 수 있습니다. Aggregates 빌더 클래스는 vectorSearch() $vectorSearch 파이프라인 단계를 만드는 데 사용할 수 있는 헬퍼 메서드를 제공합니다. 이 파이프라인 단계에서는 문서에서 시맨틱 검색 수행할 수 있습니다. 시맨틱 검색 제공된 검색 또는 구문 텀 의미는 비슷하지만 반드시 동일하지는 않은 정보를 찾는 검색 유형입니다.

중요

기능 호환성

MongoDB Vector Search는 MongoDB v,6.0.11 v 이상을 실행 Atlas 클러스터에서 근사 근접7.0.2 이웃(ANN) 검색 지원하고, MongoDB v,6.0.16 v 를 실행 클러스터에서 정확한 가장 가까운 이웃(ENN) 검색7 010지원합니다.., v..7 32이상. Atlas CLI 사용하여 생성하는 자체 관리 또는 로컬 Atlas 배포와 함께 MongoDB Vector Search를 사용할 수도 있습니다.

샘플 데이터

이 가이드의 예시에서는 Atlas 샘플 데이터 세트의 sample_mflix 데이터베이스에 있는 embedded_movies 컬렉션을 사용합니다. 무료 MongoDB Atlas cluster 를 생성하고 샘플 데이터 세트를 로드하는 방법을 학습 보려면 시작하기 튜토리얼을 참조하세요.

벡터 검색 수행

벡터 검색 쿼리를 실행 하려면 벡터 검색 인덱스 만들고 벡터 임베딩을 인덱스 해야 합니다. 프로그래밍 방식으로 벡터 검색 인덱스 만드는 방법에 대해 학습 인덱스 가이드 의 MongoDB Search 인덱스 관리 섹션을 참조하세요. 벡터 임베딩에 대해 자세히 학습 Atlas 설명서에서 벡터 검색을 위한 벡터 임베딩을 인덱스하는 방법을 참조하세요.

벡터 임베딩에 벡터 검색 인덱스 생성한 후 다음 섹션에 표시된 대로 파이프라인 단계에서 이 인덱스 참조할 수 있습니다.

벡터 검색 예제

이 예시 다음 조치를 수행하는 집계 파이프라인 빌드합니다.

plot_embedding 필드 에서 ENN 검색 실행합니다.
결과를 1 문서 로 제한
year 값이 2016이상인 문서를 일치시킵니다.
벡터 검색 점수를 다음 필드 에 투영합니다. vectorSearchScore

// Create an instance of the BinaryVector class as the query vector
float[] rawVector = new float[]{ -0.0016261312f, -0.028070757f, -0.011342932f, -0.012775794f, -0.0027440966f, 0.008683807f, -0.02575152f, -0.02020668f, -0.010283281f, -0.0041719596f, 0.021392956f, 0.028657231f, -0.006634482f, 0.007490867f, 0.018593878f, 0.0038187427f, 0.029590257f, -0.01451522f, 0.016061379f, 0.00008528442f, -0.008943722f, 0.01627464f, 0.024311995f, -0.025911469f, 0.00022596726f, -0.008863748f, 0.008823762f, -0.034921836f, 0.007910728f, -0.01515501f, 0.035801545f, -0.0035688248f, -0.020299982f, -0.03145631f, -0.032256044f, -0.028763862f, -0.0071576433f, -0.012769129f, 0.012322609f, -0.006621153f, 0.010583182f, 0.024085402f, -0.001623632f, 0.007864078f, -0.021406285f, 0.002554159f, 0.012229307f, -0.011762793f, 0.0051682983f, 0.0048484034f, 0.018087378f, 0.024325324f, -0.037694257f, -0.026537929f, -0.008803768f, -0.017767483f, -0.012642504f, -0.0062712682f, 0.0009771782f, -0.010409906f, 0.017754154f, -0.004671795f, -0.030469967f, 0.008477209f, -0.005218282f, 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BinaryVector queryVector = BinaryVector.floatVector(rawVector);
// Specify the index name for the vector embedding index
String indexName = "vector_index";
// Specify the path of the field to search on
FieldSearchPath fieldSearchPath = SearchPath.fieldPath("plot_embedding");
// Limit the number of matches to 1
long limit = 1;
// Create a pre-filter to only search within a subset of documents
VectorSearchOptions options = VectorSearchOptions.exactVectorSearchOptions()
    .filter(Filters.gte("year", 2016));
// Create the vectorSearch pipeline stage
Publisher<Document> publisher = collection.aggregate(
    Arrays.asList(
        Aggregates.vectorSearch(
            fieldSearchPath,
            queryVector,
            indexName,
            limit,
            options),
        Aggregates.project(
            Projections.metaVectorSearchScore("vectorSearchScore"))
    )
);

팁

쿼리 벡터 유형

앞의 예시 에서는 쿼리 벡터로 제공 BinaryVector 인스턴스 만들지만 Double 인스턴스의 List 을 만들 수도 있습니다. 그러나 저장 효율성 높이려면 BinaryVector 유형을 사용하는 것이 좋습니다.

다음 예시 집계 실행 하고 이전 집계 파이프라인 의 결과에서 벡터 검색 메타 점수를 출력하는 방법을 보여 줍니다.

Document found = Flux.from(publisher).blockFirst();
if (found != null) {
    double score = found.getDouble("vectorSearchScore");
    System.out.println("vectorSearch score: " + score);
}

vectorSearch score: 0.8821365833282471

API 문서

이 가이드에서 사용되는 메서드 또는 유형에 대해 자세히 알아보려면 다음 API 문서를 참조하세요.

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