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C#/ .NET 드라이버

Atlas Vector Search 쿼리 실행

개요

Atlas Vector Search를 사용하여 Atlas에 저장된 데이터에 대해 벡터 검색을 수행할 수 있습니다. 벡터 검색을 통해 키워드 일치가 아닌 시맨틱 의미를 기반으로 데이터를 쿼리하여 보다 관련성 높은 검색 결과를 조회할 수 있습니다. 이를 통해 AI 기반 애플리케이션은 검색 증강 생성(RAG)을 포함하여 시맨틱 검색, 하이브리드 검색, 생성형 검색 등의 사용 사례를 지원할 수 있습니다.

Atlas를 벡터 데이터베이스로 사용하면 Atlas에서 다른 데이터와 함께 벡터 데이터를 원활하게 인덱싱할 수 있습니다. 이를 통해 컬렉션의 필드를 필터링하고 벡터 데이터에 대해 벡터 검색 쿼리를 수행할 수 있습니다. 벡터 검색과 전체 텍스트 검색 쿼리를 결합하여 사용 사례에 가장 관련성이 높은 결과를 반환할 수 있습니다. Atlas Vector Search를 인기 있는 AI 프레임워크 및 서비스와 통합하여 애플리케이션에서 벡터 검색을 쉽게 구현할 수 있습니다.

Atlas Vector Search 에 대해 자세히 학습 MongoDB Atlas 문서에서 Atlas Vector Search 가이드 참조하세요.

샘플 데이터

이 가이드 의 예제에서는 데이터베이스 의 컬렉션 사용합니다. 이 컬렉션 에 대한 샘플 데이터 세트를 얻으려면 sample_mflix.embedded_movies sample_mflix .NET/ C# 드라이버 시작하기를 참조하세요.

이 가이드 의 예제에서는 다음 샘플 클래스를 사용하여 sample_mflix.embedded_movies 컬렉션 의 문서를 모델링합니다.

[BsonIgnoreExtraElements]
public class Movie
{
    public ObjectId Id { get; set; }
    public string Plot { get; set; }
    public string Title { get; set; }
    [BsonElement("plot_embedding")]
    public float[] PlotEmbedding { get; set; }
}

지원되는 벡터 임베딩 유형

벡터 임베딩은 데이터를 표현하기 위해 사용하는 벡터입니다. 이러한 임베딩을 통해 데이터의 의미 있는 관계를 캡처하고 시맨틱 검색 및 조회와 같은 작업이 가능해집니다.

.NET/C# 드라이버는 여러 유형의 벡터 임베딩을 지원합니다. 다음 섹션에서는 지원되는 벡터 임베딩 유형을 설명합니다.

배열 표현

.NET/C# 드라이버는 벡터 임베딩에서 배열 유형의 다음 표현을 지원합니다.

  • BsonArray

  • Memory

  • ReadOnlyMemory

  • float[] 개인정보 정책에 double[]

다음 예시 이전 유형의 속성이 있는 클래스를 보여줍니다.

public class BsonArrayVectors
{
    public BsonArray BsonArrayVector { get; set; }
    public Memory<float> MemoryVector { get; set; }
    public ReadOnlyMemory<float> ReadOnlyMemoryVector { get; set; }
    public float[] FloatArrayVector { get; set; }
}

팁

Memory 및 ReadOnlyMemory 유형 사용에 대해 자세히 학습 직렬화 가이드 의 배열 직렬화 성능 향상 섹션을 참조하세요.

바이너리 벡터 표현

.NET/C# 드라이버는 벡터 임베딩에 다음과 같은 바이너리 벡터 표현을 지원합니다.

  • BinaryVectorFloat32 (빅 엔디안 아키텍처에서는 지원되지 않음)

  • BinaryVectorInt8

  • BinaryVectorPackedBit

  • Memory<float>, Memory<byte>, Memory<sbyte>

  • ReadOnlyMemory<float>, ReadOnlyMemory<byte>, ReadOnlyMemory<sbyte>

  • float[], byte[], sbyte[]

참고

Memory<T>, ReadOnlyMemory<T> 또는 배열 유형의 바이너리 벡터 표현을 지정할 때 BinaryVector 속성을 반드시 사용해야 합니다.

다음 예시 이전 유형의 속성이 있는 클래스를 보여줍니다.

public class BinaryVectors
{
    public BinaryVectorInt8 ValuesInt8 { get; set; }
    public BinaryVectorPackedBit ValuesPackedBit { get; set; }
    public BinaryVectorFloat32 ValuesFloat { get; set; }
    [BinaryVector(BinaryVectorDataType.Int8)]
    public Memory<byte> ValuesByte { get; set; }
    [BinaryVector(BinaryVectorDataType.Float32)]
    public float[] ValuesFloat { get; set; }
}

바이너리 벡터 데이터 직렬화

Int8 바이너리 벡터 형식 데이터를 byte 또는 sbyte로 직렬화할 수 있습니다. Float32 바이너리 벡터 형식 데이터를 float로 직렬화할 수도 있습니다. 다음 예시에서는 Int8 및 Float32 바이너리 벡터 데이터를 직렬화합니다.

[BinaryVector(BinaryVectorDataType.Int8)]
public Memory<byte> ValuesByte { get; set; }
[BinaryVector(BinaryVectorDataType.Int8)]
public Memory<sbyte> ValuesSByte { get; set; }
[BinaryVector(BinaryVectorDataType.Float32)]
public float[] ValuesFloat { get; set; }

PackedBit 벡터 데이터의 byte 패딩 값이 인 경우에만 벡터 데이터를 데이터 유형 으로 표현되는 이진 벡터로 역직렬화할 수 0 있습니다. 벡터 데이터의 패딩 값이 0이 아닌 경우 BsonVectorPackedBit로만 역직렬화할 수 있습니다.

벡터 검색 쿼리 예시

VectorSearch() 메서드를 호출하여 벡터 검색 쿼리 수행할 수 있습니다. 컬렉션 에서 벡터 검색 수행하려면 먼저 벡터 데이터가 포함된 필드 와 해당 필드 다루는 벡터 검색 인덱스 있는 컬렉션 있어야 합니다.

벡터 검색을 위한 컬렉션 구성에 대해 자세히 알아보려면 MongoDB Atlas 문서의 Atlas Vector Search 가이드를 참조하세요.

ToQueryVector() 메서드를 사용하여 BinaryVectorFloat32, BinaryVectorInt8, BinaryVectorPackedBit 데이터를 BsonBinaryData 유형으로 변환하면 벡터 검색 쿼리에 사용할 수 있습니다. 다음 예시에서는 BinaryVectorInt8를 BsonBinaryData 객체로 변환합니다.

var binaryVector = new BinaryVectorInt8(new sbyte[] { 0, 1, 2, 3, 4 });
var queryVector = binaryVector.ToQueryVector();

배열로 표현된 벡터 데이터를 QueryVector 클래스의 인스턴스로 지정하여 벡터 검색 쿼리에 사용할 수 있습니다. 다음 예시는 ReadOnlyMemory<float> 값의 배열을 QueryVector 객체로 생성하여 벡터 검색 쿼리에 사용하는 방법을 보여줍니다.

QueryVector v = new QueryVector(new ReadOnlyMemory<float>([1.2f, 2.3f]));

sample_mflix 데이터베이스 의 embedded_movies 컬렉션 가정해 보겠습니다. $vectorSearch 단계를 사용하여 컬렉션 에 있는 문서의 plot_embedding 필드 에 대해 시맨틱 검색 수행할 수 있습니다. 다음 섹션에서는 이 컬렉션 에서 Atlas Vector Search 작업을 수행하는 다양한 방법에 대해 설명합니다.

팁

다음 예시 에 사용된 샘플 데이터 세트를 얻으려면 .NET/ C# 드라이버 시작하기를 참조하세요. 다음 예시 에 사용된 샘플 Atlas Vector Search 인덱스 만들려면 Atlas 매뉴얼의 Atlas Vector Search 인덱스 만들기를 참조하세요.

집계 파이프라인 예시

이 예시에서는 다음 단계를 수행하여 PlotEmbedding 필드에 벡터 데이터와 Atlas Vector Search 벡터 검색 인덱스가 포함된 컬렉션에서 Atlas Vector Search 쿼리를 실행합니다.

  1. 검색할 벡터 데이터를 배열 형태로 포함하는 배열을 생성합니다.

  2. 검색 중 사용할 인덱스 이름과 가장 가까운 이웃 수를 포함하는 VectorSearchOptions 객체를 지정합니다.

  3. VectorSearch() 단계를 사용하여 벡터 검색 쿼리 수행하고 Project() 단계를 사용하여 Title, Plot 및 Score 필드만 표시하는 집계 파이프라인 생성합니다.

  4. 쿼리 결과를 출력합니다.

// Defines vector embeddings for the string "time travel"
var vector = new[] 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// Specifies that the vector search will consider the 150 nearest neighbors
// in the specified index
var options = new VectorSearchOptions<EmbeddedMovie>()
{
   IndexName = "vector_index",
   NumberOfCandidates = 150
};
// Builds aggregation pipeline and specifies that the $vectorSearch stage
// returns 10 results
var pipeline = new EmptyPipelineDefinition<EmbeddedMovie>()
   .VectorSearch(m => m.PlotEmbedding, vector, 10, options)
   .Project(Builders<EmbeddedMovie>.Projection
      .Include(m => m.Title)
      .Include(m => m.Plot)
      .MetaVectorSearchScore(m => m.Score);

이전 예시의 결과에는 다음 문서가 포함됩니다.

{ "_id" : { "$oid" : "573a13a0f29313caabd04a4f" }, "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : 0.926971435546875 }
{ "_id" : { "$oid" : "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. 9267120361328125 }
{ "_id" : { "$oid" : "573a1399f29313caabceec0e" }, "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : 0.9235687255859375 }
{ "_id" : { "$oid" : "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" : 0.9228668212890625 }
{ "_id" : { "$oid" : "573a13aef29313caabd2e2d7" }, "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : 0.9228515625 }
{ "_id" : { "$oid" : "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" : 0.9199066162109375 }
{ "_id" : { "$oid" : "573a13c6f29313caabd715d3" }, "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : 0.919403076171875 }
{ "_id" : { "$oid" : "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" : 0.9191131591796875 }
{ "_id" : { "$oid" : "573a13b6f29313caabd477fa" }, "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : 0. 917755126953125 }
{ "_id" : { "$oid" : "573a13b3f29313caabd3ebd4" }, "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", "score" : 0.9172210693359375 }

LINQ 예제

다음 코드 샘플 앞의 예시 와 동일한 벡터 검색 쿼리 수행하지만 집계 파이프라인 구문 대신 LINQ 구문을 사용합니다.

var results = collection.AsQueryable()
    .VectorSearch(m => m.PlotEmbedding, vector, 10, options)
    .Select(m => new { m.Title, m.Plot });

추가 정보

Atlas Vector Search에 대해 자세히 알아보려면 MongoDB Atlas 문서의 Atlas Vector Search 가이드를 참조하세요.

API 문서

이 가이드 에 설명된 함수 또는 유형에 대해 자세히 학습 다음 API 설명서를 참조하세요.

돌아가기

Atlas Search

다음

시계열

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