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Fundamentals

Atlas Vector Search 쿼리 실행

이 페이지의 내용

개요

이 가이드 에서는 $vectorSearch 파이프라인 단계를 사용하여 고 (Go) 운전자 에서 Atlas Vector Search 기능 사용하는 방법을 학습 수 있습니다. 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

이 기능을 지원하는 MongoDB Atlas 버전에 대한 자세한 내용은 MongoDB Atlas 설명서의 제한 사항을 참조하세요.

샘플 데이터

이 페이지의 예시에서는 Atlas 샘플 데이터 세트의 sample_mflix 데이터베이스에 있는 embedded_movies 컬렉션에서 plot_embedding 필드 쿼리합니다.

plot_embedding 필드 에는 OpenAI의 text-embedding-ada-002 임베딩 모델을 사용하여 생성된 1536 차원의 벡터 임베딩이 포함되어 있습니다.

무료 MongoDB Atlas cluster 를 생성하고 샘플 데이터 세트를 로드하는 방법을 학습 보려면 Atlas 시작하기 가이드 를 참조하세요.

벡터 검색 수행

이 기능 사용하려면 벡터 검색 인덱스 만들고 벡터 임베딩을 인덱스 해야 합니다. 프로그래밍 방식으로 벡터 검색 인덱스 만드는 방법에 대해 학습 인덱스 가이드 의 Atlas Search 및 Atlas Vector Search 인덱스 섹션을 참조하세요. 벡터 임베딩에 대해 자세히 학습 Atlas 설명서에서 벡터 검색을 위한 벡터 임베딩을 인덱스하는 방법 을 참조하세요.

벡터 임베딩에 벡터 검색 인덱스 생성한 후 집계 파이프라인 에서 이 인덱스 참조하여 벡터 검색 쿼리 실행 수 있습니다.

다음 섹션에서는 쿼리 벡터에 대한 BSON 이진 벡터를 만드는 방법과 벡터 검색 인덱스 사용하여 plot_embedding 필드 에서 벡터 검색 쿼리 실행 방법을 보여 줍니다.

BSON 이진 벡터 만들기

이 예시 에서는 plot_embedding 필드 에서 벡터 검색 쿼리 의 쿼리 벡터로 사용할 1536 차원 벡터를 만들 수 있습니다. 쿼리 "time 여행" 문자열에 대한 벡터 임베딩을 사용하여 plot_embedding 필드 검색합니다.

다음 예시 이 벡터 임베딩을 쿼리 벡터로 사용할 수 있는 BSON 이진 벡터로 변환하는 방법을 보여 줍니다.

// Converts a float32 array to a BSON vector
vectorFloat := bson.NewVector([]float32{-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, 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-0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136})
// Converts the BSON vector to a BSON binary vector
queryVector := vectorFloat.Binary()

원본 벡터의 슬라이스에 액세스 해야 하는 경우 쿼리 벡터를 BSON 벡터로 다시 역직렬화할 수도 있습니다.

다음 예시 NewVectorFromBinary() 메서드를 사용하여 쿼리 벡터를 BSON 바이너리 벡터에서 BSON 벡터로 변환하는 방법을 보여 줍니다.

// Converts the BSON binary vector back to a BSON vector
backToVectorFloat, err := bson.NewVectorFromBinary(queryVector)
if err != nil {
	log.Fatal(err)
}

팁

쿼리 벡터 유형

앞의 예시 쿼리 벡터로 제공 BSON 이진 벡터의 인스턴스 만들지만 BSON double 값의 배열 사용할 수도 있습니다. 그러나 저장 효율성 개선하려면 BSON 이진 벡터를 사용하는 것이 좋습니다.

벡터 검색 쿼리 실행

다음 예시 $vectorSearch 및 $project 메서드를 사용하여 다음 사양으로 근사치 근접 이웃(ANN) 벡터 검색 수행하는 집계 파이프라인 빌드 방법을 보여줍니다.

  • BSON 바이너리 queryVector를 사용하여 plot_embedding 필드 쿼리합니다.

  • numCandidates 옵션을 사용하여 검색 에 사용된 가장 가까운 이웃의 수를 150 로 설정합니다.

  • plot_embedding 필드 에 생성된 vector_search 인덱스 사용합니다.

  • 지정된 plot, title 및 score 필드가 있는 5 문서를 반환합니다.

// Creates the aggregation pipeline
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "vector_index"},
		{"path", "plot_embedding"},
		{"queryVector", queryVector},
		{"numCandidates", 150},
		{"limit", 5},
	}}}
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"plot", 1},
		{"title", 1},
		{"score", bson.D{{"$meta", "vectorSearchScore"}}},
	}}}
// Runs the aggregation pipeline
cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
if err != nil {
	log.Fatalf("failed to retrieve data from the server: %v", err)
}
// Displays the results
type ProjectedMovieResult struct {
	Title string  `bson:"title"`
	Plot  string  `bson:"plot"`
	Score float64 `bson:"score"`
}
var results []ProjectedMovieResult
if err = cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score)
}
Title: Thrill Seekers
Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.
Score: 0.92730712890625
Title: About Time
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.
Score: 0.926605224609375
Title: The Time Machine
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.
Score: 0.9239959716796875
Title: Timecop
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.
Score: 0.923583984375
Title: Crusade in Jeans
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...
Score: 0.9222412109375

추가 정보

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

파이프라인 단계의 구문에 대해 자세히 학습 $vectorSearch MongoDB Atlas 문서의 Atlas Vector Search 섹션에 있는 가이드 만들기 및 실행 가이드의 구문 및 필드 섹션을 참조하세요.

API 문서

이 가이드에서 설명하는 메서드나 유형에 대해 자세히 알아보려면 다음 API 설명서를 참조하세요.

돌아가기

CRUD 실행 수정

다음

집계