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MongoDB 벡터 검색 쿼리 실행

개요

이 가이드에서는 $vectorSearch 파이프라인 단계를 사용해 Go 드라이버에서 MongoDB Vector Search 기능을 사용하는 방법을 알아볼 수 있습니다. 이 파이프라인 단계에서는 문서에서 시맨틱 검색을 수행할 수 있습니다. 시맨틱 검색은 제공된 검색어 또는 구문과 의미가 유사하지만 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 클러스터 생성하고 샘플 데이터 세트를 로드하는 방법을 학습 보려면 Atlas 시작하기를 참조하세요.

MongoDB 벡터 검색 쿼리 실행

이 기능 사용하려면 벡터 검색 인덱스 만들고 벡터 임베딩을 인덱스 해야 합니다. 프로그래밍 방식으로 벡터 검색 인덱스 만드는 방법에 대한 학습은 MongoDB Search 및 MongoDB Vector Search 인덱스를 참조하세요. 벡터 임베딩에 대해 자세히 학습하려면 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, 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, 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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)
}

자동 임베딩 인덱스 쿼리

자동 임베딩 MongoDB Vector Search 인덱스 쿼리하여 텍스트 검색을 위한 벡터 생성을 자동화할 수 있습니다. Go 드라이버 사용하여 자동 임베딩 인덱스 만드는 방법을 학습하려면 MongoDB 자동 임베딩 검색 인덱스 모델 섹션을 인덱스 가이드에서 참조하세요.

다음 예시 plot 필드 에서 "time travel" 구문과의 의미적 유사성을 검색하는 집계 파이프라인 빌드합니다. 이 쿼리 auto_embedding_index이라는 plot 필드 에 자동 임베딩된 MongoDB 벡터 검색 인덱스 사용합니다.

// Creates the $vectorSearch stage that queries the
// auto-embedding index named "auto_embedding_index"
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "auto_embedding_index"},
		{"path", "plot"},
		{"query", bson.D{{"text", "time travel"}}},
		{"model", "voyage-4"},
		{"numCandidates", 150},
		{"limit", 10},
	}},
}
// Projects only the title and plot fields
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"title", 1},
		{"plot", 1},
	}},
}
// 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)
}
defer cursor.Close(ctx)
type ProjectedMovieResult struct {
	Title string `bson:"title"`
	Plot  string `bson:"plot"`
}
var results []ProjectedMovieResult
if err := cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v\nPlot: %v\n---\n\n", result.Title, result.Plot)
}

참고

자동 임베딩 MongoDB Vector Search 인덱스 쿼리 때는 query 필드 에 검색 할 텍스트( 예시 : {"text", "time travel"})를 입력하고 model 필드 에 호환되는 모델 이름을 입력합니다. 지원되는 모델은 voyage-4-lite, voyage-4, voyage-4-large, voyage-code-3입니다. queryVector 값을 계산하거나 전달할 필요가 없습니다.

추가 정보

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

$vectorSearch 파이프라인 단계의 구문에 대해 자세히 알아보려면 MongoDB Atlas 설명서의 MongoDB Vector Search 섹션에 있는 쿼리 생성 및 실행 가이드의 구문 및 필드 섹션을 참조하세요.

API 문서

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

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