knnBeta
정의
knnBeta
knnBeta
연산자는 HNSW(Hierarchical Navigable Small Worlds) 알고리즘을 사용하여 시맨틱 검색을 수행합니다. kNN 쿼리에 대한 Atlas Search 지원을 사용하여 선택한 제품과 유사한 검색, 이미지 검색 등을 수행할 수 있습니다.
구문
knnBeta
의 구문은 다음과 같습니다:
1 { 2 $search: { 3 "index": "<index name>", // optional, defaults to "default" 4 "knnBeta": { 5 "vector": [<array-of-numbers>], 6 "path": "<field-to-search>", 7 "filter": {<filter-specification>}, 8 "k": <number>, 9 "score": {<options>} 10 } 11 } 12 }
옵션
행동
Atlas Search 유형 knnVector로 인덱싱된 필드에 대해서만 kNN 쿼리를 실행할 수 있습니다.
단계 뒤에 를 사용하여 쿼리 결과의 문서 수를 제한할 수 $limit
있습니다. 값을 값보다 $search
knnBeta
k
높게 설정하는 $limit
것이 좋습니다. 이러한 과다 요청 패턴은 대략적인 근접 이웃 검색에서 지연 시간과 리콜의 균형을 맞출 수 있는 주요 방법입니다. 경험상 - 의 배수는 510 많은 사용 사례에서 잘 작동하는 것으로 나타났지만, 특정 데이터 세트에 맞게 이 배수를 조정하는 것이 좋습니다.
예시
다음 쿼리는 쿼리에 대해 150
개의 가장 근접한 이웃을 찾고 나머지 결과 수를 50
개로 제한합니다.
1 db.<collection>.aggregate({ 2 "$search": { 3 "knnBeta": { 4 "vector": <array-of-numbers-to-search>, 5 "path": <indexed-field-to-search>, 6 "k": 150 7 } 8 } 9 }, 10 { 11 "$limit": 50 12 })
성능
쿼리 성능을 향상시키려면 결과에 모든 필드가 필요한 경우를 제외하고 $project
단계를 사용하여 결과에 반환할 필드를 선택합니다. $project
단계에서 벡터 필드를 제외하는 것이 좋습니다.
득점
단계에서 score
필드를 $meta 표현식 searchScore
와 함께 $project
사용하여 결과에서 문서의 점수를 반환할 수 있습니다.
Atlas Search는 0
에서 1
사이의 고정된 범위에서만 kNN 쿼리에 대한 결과의 점수를 매깁니다. cosine
및 dotProduct
유사도에 대해서는 Atlas Search가 다음 알고리즘을 사용하여 점수를 정규화합니다:
score = (1 + cosine/dot_product(v1,v2)) / 2
제한 사항
knnBeta
연산자는 쿼리의 최상위 연산자여야 하므로, 다음과 같은 연산자 내에서 knnBeta
연산자를 사용할 수 없습니다.
vectorSearch 유형 인덱스를 사용하여 인덱싱된 필드에는 knnBeta
연산자를 사용하여 쿼리할 수 없습니다. knnBeta
연산자와 함께 $search
정렬 옵션을 사용할 수 없습니다.
단계 이후에는 $skip
$limit
$search
및 를 사용하여 Atlas Search 결과 페이지 매김을 하지 않는 것이 좋습니다.
예시
다음 쿼리는 knnBeta
연산자를 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 컬렉션을 Atlas 클러스터에 추가하고 컬렉션에 대한 샘플 인덱스 정의를 만든 경우 sample_mflix
데이터베이스로 전환하여 컬렉션에 대해 다음 쿼리를 실행할 수 있습니다.
다음 쿼리는 knnBeta
연산자를 사용하여 plot_embedding
필드에서 vector
차원을 검색하고 결과에서 최대 5
가장 가까운 이웃을 요청합니다. 이 쿼리는 또한 $project
단계를 지정하여 다음을 수행합니다.
_id
필드를 제외하고plot
,title
필드를 결과에 포함합니다.결과에
score
이라는 필드를 추가합니다.
1 db.embedded_movies.aggregate([ 2 { 3 "$search": { 4 "index": "default", 5 "knnBeta": { 6 "vector": 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7 "path": "plot_embedding", 8 "k": 5 9 } 10 } 11 }, 12 { 13 "$project": { 14 "_id": 0, 15 "plot": 1, 16 "title": 1, 17 "score": { $meta: "searchScore" } 18 } 19 } 20 ])
1 [ 2 { 3 plot: 'When two girls move to the country to be near their ailing mother, they have adventures with the wonderous forest spirits who live nearby.', 4 title: 'My Neighbor Totoro', 5 score: 0.9999986886978149 6 }, 7 { 8 plot: "To escape a war, a girl flees to a remote farmhouse and becomes part of an expansive family's unusual, perhaps even supernatural lifestyle.", 9 title: 'Black Moon', 10 score: 0.8282386660575867 11 }, 12 { 13 plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.', 14 title: 'Forest Warrior', 15 score: 0.8196926712989807 16 }, 17 { 18 plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.', 19 title: 'Forest Warrior', 20 score: 0.8196901082992554 21 }, 22 { 23 plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.', 24 title: 'Coraline', 25 score: 0.815956711769104 26 } 27 ]
다음 쿼리는 6월 1971 (1971-06-30
) 이전 또는 (≤
) 30 이전에 개봉한 영화의 문서를 필터링한 다음, 필터링된 문서의 plot_embedding
필드에서 vector
차원을 검색하고 요청합니다. 결과에서 최대 5
가장 가까운 이웃 이 쿼리는 또한 $project
단계를 지정하여 다음을 수행합니다.
_id
필드를 제외하고 결과에plot
,title
및released
필드만 포함합니다.결과에
score
이라는 필드를 추가합니다.
1 db.embedded_movies.aggregate([ 2 { 3 "$search": { 4 "index": "default", 5 "knnBeta": { 6 "vector": 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7 "path": "plot_embedding", 8 "filter": { 9 "range": { 10 "lte": ISODate("1971-06-30 00:27:59.177000000"), 11 "path": "released" 12 } 13 }, 14 "k": 5 15 } 16 } 17 }, 18 { 19 "$project": { 20 "_id": 0, 21 "title": 1, 22 "plot": 1, 23 "released": 1, 24 "score": { $meta: "searchScore" } 25 } 26 } 27 ])
1 [ 2 { 3 plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.', 4 title: 'Willy Wonka & the Chocolate Factory', 5 released: ISODate("1971-06-30T00:00:00.000Z"), 6 score: 0.9999985694885254 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 released: ISODate("1968-12-18T00:00:00.000Z"), 12 score: 0.7576897740364075 13 }, 14 { 15 plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.', 16 title: 'The Red Balloon', 17 released: ISODate("1957-03-11T00:00:00.000Z"), 18 score: 0.7515948414802551 19 }, 20 { 21 plot: 'While hearing the story of "Gulliver's Travels", a young boy dreams he is the title character on the island of Lilliput .', 22 title: 'The New Gulliver', 23 released: ISODate("1935-11-03T00:00:00.000Z"), 24 score: 0.7495509386062622 25 }, 26 { 27 plot: 'A living puppet, with the help of a cricket as his conscience, must prove himself worthy to become a real boy.', 28 title: 'Pinocchio', 29 released: ISODate("1940-02-23T00:00:00.000Z"), 30 score: 0.7472590804100037 31 } 32 ]
Atlas Search는 1971-06-30
보다 작거나 같은 released
필드 값을 기준으로 문서를 필터링합니다. 제목이 Willy Wonka & the Chocolate Factory
인 문서가 filter
옵션의 검색 기준 및 쿼리의 vector
차원과 일치하기 때문에 더 높은 점수를 줍니다.