Atlas Vector Search 및 Atlas Search를 사용하여 하이브리드 검색 수행
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Atlas Vector Search와 Atlas Search 쿼리를 결합하여 통합된 결과를 위한 하이브리드 검색을 수행할 수 있습니다.
이 자습서에서는 영화에 대한 세부 정보가 포함된 sample_mflix.embedded_movies 컬렉션에서 하이브리드 검색을 실행하는 방법을 보여 줍니다. 이 자습서에서는 다음 단계를 안내합니다.
plot_embedding필드에 Atlas Vector Search 인덱스를 생성합니다. 이 필드에는 영화 줄거리 요약을 나타내는 벡터 임베딩이 포함되어 있습니다.Atlas Search 인덱스를
sample_mflix.embedded_movies컬렉션의title필드에 생성합니다. 이 필드에는 영화 이름이 텍스트 문자열로 포함되어 있습니다.상호 순위 결합을 사용하여
plot_embedding필드에 대한$vectorSearch쿼리와title필드에 대한$search쿼리의 결과를 결합하는 쿼리를 실행합니다.
하이브리드 검색이 필요한 이유
하이브리드 검색은 동일한 쿼리 조건에 대해 전체 텍스트 검색과 시맨틱 검색과 같은 다양한 검색 방법을 결합한 것입니다. 전체 텍스트 검색은 쿼리 텀에 대한 정확한 일치를 찾는 데 효과적인 반면, 시맨틱 검색은 문서에 정확한 쿼리 텀이 포함되어 있지 않더라도 의미적으로 유사한 문서를 식별할 수 있는 추가 이점을 제공합니다. 이를 통해 동의어나 맥락적으로 유사한 일치 항목도 두 가지 검색 방법의 결합 결과에 포함될 수 있습니다.
반대로 데이터 세트에 고유 명사나 특정 키워드에 대한 토큰이 포함되어 있다면 이러한 토큰이 데이터 세트에서 사용되는 동일한 맥락에서 임베딩 모델의 학습에 고려되지 않기를 원할 수 있습니다. 이 경우 벡터 검색을 전체 텍스트 검색과 결합하면 더 나은 결과를 얻을 수 있습니다.
또한 쿼리별로 각 검색 방법에 대한 가중치를 설정할 수 있습니다. 전체 텍스트 또는 시맨틱 검색 결과가 쿼리에 가장 관련성이 높고 적절한지 여부에 따라, 쿼리별로 해당 검색 방법의 가중치를 높일 수 있습니다.
상호 순위 결합 소개
상호 순위 결합은 서로 다른 검색 방법(예: 시맨틱 검색, 전체 텍스트 검색)의 결과를 다음 작업을 통해 단일 결과 집합으로 결합하는 기술입니다.
결과에서 문서의 상호 순위를 계산합니다.
각 검색 결과에서 순위가 매겨진 각 문서에 대해 먼저 상수
60으로 문서의 순위(r)를 더하여 점수(rank_constant)를 평활화한 후1을r및rank_constant의 합으로 나누어 결과 내 문서의 상호 순위를 구합니다.reciprocal_rank = 1 / ( r + rank_constant ) 각 검색 방법에 대해 서로 다른 가중치(
w)를 적용하여 해당 검색 방법에 더 많은 중요성을 부여합니다. 각 문서에 대해 가중치가 적용된 상호 순위는 가중치에 문서의 상호 순위를 곱하여 계산됩니다.weighted_reciprocal_rank = w x reciprocal_rank 결과에서 문서의 순위 도출 점수 및 가중치 점수를 결합합니다.
모든 검색 결과의 각 문서에 해당 문서의 단일 점수에 대해 계산된 상호 순위를 추가합니다.
결과에 있는 문서의 합산 점수를 기준으로 결과를 정렬합니다.
결과 내 문서를 단일, 통합된 순위 목록으로 정렬하기 위해 결과 전반에 걸쳐 결합된 점수를 기준으로 문서를 정렬합니다.
전제 조건
시작하기 전에 다음이 필요합니다.
MongoDB 버전이 v6.0.11 또는 v7.0.2 이상인 Atlas 클러스터.
참고
Atlas cluster에 Atlas Search와 Atlas Vector Search 인덱스를 모두 저장하고 성능이 뛰어난 쿼리를 실행할 수 있는 충분한 메모리가 있는지 확인하세요.
Atlas 클러스터에 로드된 샘플 데이터
다음 클라이언트 중 하나를 사용하여 Atlas cluster 에서 쿼리를 실행 합니다.
Project Data Access Admin의 프로젝트 액세스 권한 - Atlas Vector Search와 Atlas Search 인덱스 생성 목적.
Atlas Vector Search 및 Atlas Search 인덱스 만들기
이 섹션에서는 sample_mflix.embedded_movies collection의 필드에 다음 인덱스를 생성하는 방법을 보여 줍니다.
해당 필드에 대해 벡터 쿼리를 실행하기 위한
plot_embedding필드에 대한 Atlas Vector Search 인덱스입니다.해당 필드에 대해 전체 텍스트 검색을 실행하기 위한
title필드에 대한 Atlas Search 검색 인덱스입니다.
절차
1
Atlas Vector Search 인덱스를 정의합니다.
다음 인덱스 정의는 컬렉션을 쿼리할 때 plot_embedding 필드를 Atlas Vector Search 필드로 인덱싱합니다.
mongosh v2.1.2 이상을 사용하여 컬렉션에 대한 Atlas Vector Search 인덱스를 만들려면 다음 단계를 수행하세요.
mongosh를 사용하여 Atlas 클러스터에 연결합니다.자세한 내용은
mongosh를 통한 연결을 참조하세요.인덱스를 생성하려는 컬렉션이 포함된 데이터베이스로 전환합니다.
use sample_mflix switched to db sample_mflix db.collection.createSearchIndex()메서드를 실행합니다.1 db.embedded_movies.createSearchIndex( 2 "rrf-vector-search", 3 "vectorSearch", 4 { 5 "fields": [ 6 { 7 "type": "vector", 8 "path": "plot_embedding", 9 "numDimensions": 1536, 10 "similarity": "dotProduct" 11 } 12 ] 13 } 14 );
2
Atlas Search 인덱스를 정의합니다.
다음 인덱스 정의는 필드를 쿼리하기 위한 문자열 유형으로 title 필드를 인덱싱합니다.
mongosh v2.1.2 이상을 사용하여 컬렉션의 Atlas Search 인덱스를 만들려면 다음 단계를 수행하세요.
mongosh를 사용하여 Atlas 클러스터에 연결합니다.자세한 내용은
mongosh를 통한 연결을 참조하세요.인덱스를 생성하려는 컬렉션이 포함된 데이터베이스로 전환합니다.
use sample_mflix switched to db sample_mflix db.collection.createSearchIndex()메서드를 실행합니다.1 db.embedded_movies.createSearchIndex( 2 "rrf-full-text-search", 3 "search", 4 { 5 "mappings": { 6 "dynamic": false, 7 "fields": { 8 "title": [{ 9 "type": "string" 10 }] 11 } 12 } 13 } 14 );
1
환경 설정
Node.js 프로젝트를 초기화합니다.
터미널에서 다음 명령을 실행하여
hybrid-search-mongodb라는 새 디렉터리를 만들고 프로젝트를 초기화합니다.mkdir hybrid-search-mongodb cd hybrid-search-mongodb npm init -y 종속성을 설치하고 가져옵니다.
다음 명령을 실행합니다:
npm install mongodb package.json파일을 업데이트합니다.다음 예시와 같이 프로젝트의
package.json파일에서type필드를 지정한 다음 파일을 저장합니다.{ "name": "hybrid-search-mongodb", "type": "module", ... .env파일을 만듭니다.프로젝트에서 Atlas 연결 문자열을 저장할
.env파일을 생성하세요.ATLAS_CONNECTION_STRING = "<connection-string>"; <connection-string>자리 표시자 값을 Atlas cluster 에 대한SRV 연결 문자열 로 바꿉니다. 연결 문자열 다음 형식을 사용해야 합니다.mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net 참고
최소 Node.js 버전 요구 사항
Node.js v20.x
--env-file옵션을 도입했습니다. 이전 버전의 Node.js를 사용하는 경우 프로젝트에dotenv패키지를 추가하거나 다른 방법으로 환경 변수를 관리하세요.
2
Atlas Vector Search 인덱스를 정의합니다.
다음 인덱스 정의는 컬렉션을 쿼리할 때 plot_embedding 필드를 Atlas Vector Search 필드로 인덱싱합니다.
avs-index.js파일을 생성하고 다음 코드를 붙여넣습니다.import { MongoClient } from 'mongodb'; // Connect to your Atlas deployment const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); async function run() { try { const database = client.db("sample_mflix"); const collection = database.collection("embedded_movies"); // Define your Atlas Vector Search index const index = { name: "rrf-vector-search", type: "vectorSearch", definition: { "fields": [ { "type": "vector", "numDimensions": 1536, "path": "plot_embedding", "similarity": "dotProduct" } ] } } // Call the method to create the index const result = await collection.createSearchIndex(index); console.log(result); } finally { await client.close(); } } run().catch(console.dir); 다음 명령을 실행하여 코드를 실행합니다.
node --env-file=.env avs-index.js Atlas에서 인덱스를 생성하는 데 몇 분이 걸릴 수 있습니다. 인덱스를 사용할 준비가 되면 Atlas에서 이메일을 보냅니다. Atlas UI에서 새로 생성된 인덱스는 Atlas Search 탭에 나타납니다. 인덱스가 빌드되는 동안 Status 필드는 Build in Progress를 읽습니다. 인덱스 빌드가 완료되면 Status 필드에 Active가 표시됩니다.
3
Atlas Search 인덱스를 정의합니다.
다음 인덱스 정의는 필드를 쿼리하기 위한 문자열 유형으로 title 필드를 인덱싱합니다.
fts-index.js파일을 생성하고 다음 코드를 붙여넣습니다.import { MongoClient } from 'mongodb'; // Connect to your Atlas deployment const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); async function run() { try { const database = client.db("sample_mflix"); const collection = database.collection("embedded_movies"); // Define your Atlas Search index const index = { name: "rrf-full-text-search", type: "search", definition: { "mappings": { "dynamic": false, "fields": { "title": [{ "type": "string" }] } } } } // Call the method to create the index const result = await collection.createSearchIndex(index); console.log(result); } finally { await client.close(); } } run().catch(console.dir); 다음 명령을 실행하여 코드를 실행합니다.
node --env-file=.env fts-index.js Atlas에서 인덱스를 생성하는 데 몇 분이 걸릴 수 있습니다. 인덱스를 사용할 준비가 되면 Atlas에서 이메일을 보냅니다. Atlas UI에서 새로 생성된 인덱스는 Atlas Search 탭에 나타납니다. 인덱스가 빌드되는 동안 Status 필드는 Build in Progress를 읽습니다. 인덱스 빌드가 완료되면 Status 필드에 Active가 표시됩니다.
결합된 시맨틱 검색 및 전체 텍스트 검색 쿼리 실행
이 섹션에서는 $vectorSearch 및 $search 파이프라인 단계를 사용하여 plot_embedding 및 title 필드의 스타워즈에 대한 sample_mflix.embedded_movies 컬렉션의 데이터를 쿼리하고 두 단계의 각 문서 점수를 결합하여 결과에서 문서를 정렬하는 방법을 보여 줍니다. 이렇게 하면 두 검색에 모두 나타나는 문서가 결합된 결과의 맨 위에 표시됩니다.
절차
1
mongosh를 사용하여 클러스터에 연결합니다.
터미널 창에서 mongosh 를 열고 클러스터에 연결합니다. 연결에 대한 자세한 지침은 mongosh 를 통한 연결을 참조하세요.
2
sample_mflix 데이터베이스를 사용합니다.
mongosh 프롬프트에서 다음 명령을 실행합니다.
use sample_mflix
switched to db sample_mflix
3
embedded_movies 컬렉션에 대해 다음 Atlas Search 쿼리를 실행합니다.
1 var vector_weight = 0.1; 2 var full_text_weight = 0.9; 3 db.embedded_movies.aggregate([ 4 { 5 "$vectorSearch": { 6 "index": "rrf-vector-search", 7 "path": "plot_embedding", 8 "queryVector": 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9 "numCandidates": 100, 10 "limit": 20 11 } 12 }, { 13 "$group": { 14 "_id": null, 15 "docs": {"$push": "$$ROOT"} 16 } 17 }, { 18 "$unwind": { 19 "path": "$docs", 20 "includeArrayIndex": "rank" 21 } 22 }, { 23 "$addFields": { 24 "vs_score": { 25 "$multiply": [ 26 vector_weight, { 27 "$divide": [ 28 1.0, { 29 "$add": ["$rank", 60] 30 } 31 ] 32 } 33 ] 34 } 35 } 36 }, { 37 "$project": { 38 "vs_score": 1, 39 "_id": "$docs._id", 40 "title": "$docs.title" 41 } 42 }, 43 { 44 "$unionWith": { 45 "coll": "embedded_movies", 46 "pipeline": [ 47 { 48 "$search": { 49 "index": "rrf-full-text-search", 50 "phrase": { 51 "query": "star wars", 52 "path": "title" 53 } 54 } 55 }, { 56 "$limit": 20 57 }, { 58 "$group": { 59 "_id": null, 60 "docs": {"$push": "$$ROOT"} 61 } 62 }, { 63 "$unwind": { 64 "path": "$docs", 65 "includeArrayIndex": "rank" 66 } 67 }, { 68 "$addFields": { 69 "fts_score": { 70 "$multiply": [ 71 full_text_weight, { 72 "$divide": [ 73 1.0, { 74 "$add": ["$rank", 60] 75 } 76 ] 77 } 78 ] 79 } 80 } 81 }, 82 { 83 "$project": { 84 "fts_score": 1, 85 "_id": "$docs._id", 86 "title": "$docs.title" 87 } 88 } 89 ] 90 } 91 }, 92 { 93 "$group": { 94 "_id": "$_id", 95 "title": {"$first": "$title"}, 96 "vs_score": {"$max": "$vs_score"}, 97 "fts_score": {"$max": "$fts_score"} 98 } 99 }, 100 { 101 "$project": { 102 "_id": 1, 103 "title": 1, 104 "vs_score": {"$ifNull": ["$vs_score", 0]}, 105 "fts_score": {"$ifNull": ["$fts_score", 0]} 106 } 107 }, 108 { 109 "$project": { 110 "score": {"$add": ["$fts_score", "$vs_score"]}, 111 "_id": 1, 112 "title": 1, 113 "vs_score": 1, 114 "fts_score": 1 115 } 116 }, 117 {"$sort": {"score": -1}}, 118 {"$limit": 10} 119 ])
{ _id: new ObjectId('573a13c0f29313caabd62f62'), title: 'Star Wars: The Clone Wars', vs_score: 0.0015625, fts_score: 0.015, score: 0.0165625 } { _id: new ObjectId('573a1397f29313caabce68f6'), title: 'Star Wars: Episode IV - A New Hope', vs_score: 0.0016666666666666668, fts_score: 0.014754098360655738, score: 0.016420765027322405 } { _id: new ObjectId('573a139af29313caabcf0f5f'), title: 'Star Wars: Episode I - The Phantom Menace', vs_score: 0.0016393442622950822, fts_score: 0.014516129032258065, score: 0.016155473294553146 } { _id: new ObjectId('573a1397f29313caabce77d9'), title: 'Star Wars: Episode V - The Empire Strikes Back', vs_score: 0.0016129032258064516, fts_score: 0.014285714285714285, score: 0.015898617511520736 } { _id: new ObjectId('573a1397f29313caabce8cdb'), title: 'Star Wars: Episode VI - Return of the Jedi', vs_score: 0.0015873015873015873, fts_score: 0.0140625, score: 0.015649801587301587 } { _id: new ObjectId('573a139af29313caabcf124d'), title: 'Star Wars: Episode III - Revenge of the Sith', vs_score: 0.0013698630136986301, fts_score: 0.013846153846153847, score: 0.015216016859852476 } { _id: new ObjectId('573a139af29313caabcf1258'), title: 'Star Wars: Episode II - Attack of the Clones', vs_score: 0.0014925373134328358, fts_score: 0.013636363636363637, score: 0.015128900949796473 } { _id: new ObjectId('573a1397f29313caabce6f53'), title: 'Message from Space', vs_score: 0.0015384615384615387, fts_score: 0, score: 0.0015384615384615387 } { _id: new ObjectId('573a139df29313caabcfa90b'), title: 'Message from Space', vs_score: 0.0015151515151515154, fts_score: 0, score: 0.0015151515151515154 } { _id: new ObjectId('573a13d9f29313caabda92ff'), title: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 }
117 행의 score 값을 1 값으로 대체하여 결과를 오름차순으로 정렬하면 Atlas Vector Search에서 다음과 같은 결과를 반환합니다.
{ _id: new ObjectId('573a13b0f29313caabd34a3e'), title: 'Cowboys & Aliens', vs_score: 0.0012658227848101266, fts_score: 0, score: 0.0012658227848101266 } { _id: new ObjectId('573a13a3f29313caabd0ec59'), title: 'Planet of the Apes', vs_score: 0.001282051282051282, fts_score: 0, score: 0.001282051282051282 } { _id: new ObjectId('573a139bf29313caabcf3d4b'), title: 'Planet of the Apes', vs_score: 0.001298701298701299, fts_score: 0, score: 0.001298701298701299 } { _id: new ObjectId('573a1397f29313caabce7509'), title: 'Starcrash', vs_score: 0.0013157894736842105, fts_score: 0, score: 0.0013157894736842105 } { _id: new ObjectId('573a13b0f29313caabd33d15'), title: 'Zathura: A Space Adventure', vs_score: 0.0013333333333333335, fts_score: 0, score: 0.0013333333333333335 } { _id: new ObjectId('573a1398f29313caabce8d67'), title: 'Space Raiders', vs_score: 0.0013513513513513514, fts_score: 0, score: 0.0013513513513513514 } { _id: new ObjectId('573a1398f29313caabce90bd'), title: 'The Ewok Adventure', vs_score: 0.001388888888888889, fts_score: 0, score: 0.001388888888888889 } { _id: new ObjectId('573a1398f29313caabce9091'), title: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 } { _id: new ObjectId('573a13d4f29313caabd9887f'), title: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 } { _id: new ObjectId('573a13d5f29313caabd9c312'), title: 'Guardians of the Galaxy', vs_score: 0.0014492753623188406, fts_score: 0, score: 0.0014492753623188406 }
1
결합된 쿼리 파이프라인을 정의합니다.
combined-query.js 파일을 생성하고 다음 코드를 붙여넣습니다.
1 import { MongoClient } from 'mongodb'; 2 3 async function run() { 4 // Connect to your Atlas cluster 5 const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); 6 7 try { 8 await client.connect(); 9 const database = client.db("sample_mflix"); 10 const collection = database.collection("embedded_movies"); 11 12 const vectorWeight = 0.1; 13 const fullTextWeight = 0.9; 14 const pipeline = [ 15 { 16 "$vectorSearch": { 17 "index": "rrf-vector-search", 18 "path": "plot_embedding", 19 "queryVector": 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20 "numCandidates": 100, 21 "limit": 20 22 } 23 }, { 24 "$group": { 25 "_id": null, 26 "docs": {"$push": "$$ROOT"} 27 } 28 }, { 29 "$unwind": { 30 "path": "$docs", 31 "includeArrayIndex": "rank" 32 } 33 }, { 34 "$addFields": { 35 "vs_score": { 36 "$multiply": [ 37 vectorWeight, { 38 "$divide": [ 39 1.0, { 40 "$add": ["$rank", 60] 41 } 42 ] 43 } 44 ] 45 } 46 } 47 }, { 48 "$project": { 49 "vs_score": 1, 50 "_id": "$docs._id", 51 "title": "$docs.title" 52 } 53 }, { 54 "$unionWith": { 55 "coll": "embedded_movies", 56 "pipeline": [ 57 { 58 "$search": { 59 "index": "rrf-full-text-search", 60 "phrase": { 61 "query": "star wars", 62 "path": "title" 63 } 64 } 65 }, { 66 "$limit": 20 67 }, { 68 "$group": { 69 "_id": null, 70 "docs": {"$push": "$$ROOT"} 71 } 72 }, { 73 "$unwind": { 74 "path": "$docs", 75 "includeArrayIndex": "rank" 76 } 77 }, { 78 "$addFields": { 79 "fts_score": { 80 "$multiply": [ 81 fullTextWeight, { 82 "$divide": [ 83 1.0, { 84 "$add": ["$rank", 60] 85 } 86 ] 87 } 88 ] 89 } 90 } 91 }, 92 { 93 "$project": { 94 "fts_score": 1, 95 "_id": "$docs._id", 96 "title": "$docs.title" 97 } 98 } 99 ] 100 } 101 }, { 102 "$group": { 103 "_id": "$_id", 104 "title": {"$first": "$title"}, 105 "vs_score": {"$max": "$vs_score"}, 106 "fts_score": {"$max": "$fts_score"} 107 } 108 }, { 109 "$project": { 110 "_id": 1, 111 "title": 1, 112 "vs_score": {"$ifNull": ["$vs_score", 0]}, 113 "fts_score": {"$ifNull": ["$fts_score", 0]} 114 } 115 }, { 116 "$project": { 117 "score": {"$add": ["$fts_score", "$vs_score"]}, 118 "_id": 1, 119 "title": 1, 120 "vs_score": 1, 121 "fts_score": 1 122 } 123 }, 124 {"$sort": {"score": -1}}, 125 {"$limit": 10} 126 ]; 127 128 // run pipeline 129 const result = collection.aggregate(pipeline); 130 131 for await (const doc of result) { 132 console.log(doc); 133 } 134 } catch (err) { 135 console.log(err.stack); 136 } 137 finally { 138 await client.close(); 139 } 140 } 141 run().catch(console.dir);
2
embedded_movies 컬렉션에 대해 쿼리를 실행하는 코드를 실행합니다.
node --env-file=.env combined-query.js
{ _id: new ObjectId('573a13c0f29313caabd62f62'), title: 'Star Wars: The Clone Wars', vs_score: 0.0015625, fts_score: 0.015, score: 0.0165625 } { _id: new ObjectId('573a1397f29313caabce68f6'), title: 'Star Wars: Episode IV - A New Hope', vs_score: 0.0016666666666666668, fts_score: 0.014754098360655738, score: 0.016420765027322405 } { _id: new ObjectId('573a139af29313caabcf0f5f'), title: 'Star Wars: Episode I - The Phantom Menace', vs_score: 0.0016393442622950822, fts_score: 0.014516129032258065, score: 0.016155473294553146 } { _id: new ObjectId('573a1397f29313caabce77d9'), title: 'Star Wars: Episode V - The Empire Strikes Back', vs_score: 0.0016129032258064516, fts_score: 0.014285714285714285, score: 0.015898617511520736 } { _id: new ObjectId('573a1397f29313caabce8cdb'), title: 'Star Wars: Episode VI - Return of the Jedi', vs_score: 0.0015873015873015873, fts_score: 0.0140625, score: 0.015649801587301587 } { _id: new ObjectId('573a139af29313caabcf124d'), title: 'Star Wars: Episode III - Revenge of the Sith', vs_score: 0.0013698630136986301, fts_score: 0.013846153846153847, score: 0.015216016859852476 } { _id: new ObjectId('573a139af29313caabcf1258'), title: 'Star Wars: Episode II - Attack of the Clones', vs_score: 0.0014925373134328358, fts_score: 0.013636363636363637, score: 0.015128900949796473 } { _id: new ObjectId('573a1397f29313caabce6f53'), title: 'Message from Space', vs_score: 0.0015384615384615387, fts_score: 0, score: 0.0015384615384615387 } { _id: new ObjectId('573a139df29313caabcfa90b'), title: 'Message from Space', vs_score: 0.0015151515151515154, fts_score: 0, score: 0.0015151515151515154 } { _id: new ObjectId('573a13d9f29313caabda92ff'), title: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 }
124 행의 score 값을 1 값으로 대체하여 결과를 오름차순으로 정렬하면 Atlas Vector Search에서 다음과 같은 결과를 반환합니다.
{ _id: new ObjectId('573a13b0f29313caabd34a3e'), title: 'Cowboys & Aliens', vs_score: 0.0012658227848101266, fts_score: 0, score: 0.0012658227848101266 } { _id: new ObjectId('573a13a3f29313caabd0ec59'), title: 'Planet of the Apes', vs_score: 0.001282051282051282, fts_score: 0, score: 0.001282051282051282 } { _id: new ObjectId('573a139bf29313caabcf3d4b'), title: 'Planet of the Apes', vs_score: 0.001298701298701299, fts_score: 0, score: 0.001298701298701299 } { _id: new ObjectId('573a1397f29313caabce7509'), title: 'Starcrash', vs_score: 0.0013157894736842105, fts_score: 0, score: 0.0013157894736842105 } { _id: new ObjectId('573a13b0f29313caabd33d15'), title: 'Zathura: A Space Adventure', vs_score: 0.0013333333333333335, fts_score: 0, score: 0.0013333333333333335 } { _id: new ObjectId('573a1398f29313caabce8d67'), title: 'Space Raiders', vs_score: 0.0013513513513513514, fts_score: 0, score: 0.0013513513513513514 } { _id: new ObjectId('573a1398f29313caabce90bd'), title: 'The Ewok Adventure', vs_score: 0.001388888888888889, fts_score: 0, score: 0.001388888888888889 } { _id: new ObjectId('573a1398f29313caabce9091'), title: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 } { _id: new ObjectId('573a13d4f29313caabd9887f'), title: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 } { _id: new ObjectId('573a13d5f29313caabd9c312'), title: 'Guardians of the Galaxy', vs_score: 0.0014492753623188406, fts_score: 0, score: 0.0014492753623188406 }
쿼리 정보
샘플 쿼리는 시맨틱 검색과 전체 텍스트 검색에서 정렬된 검색 결과를 조회하고, 결과 배열에서의 문서 위치에 따라 결과에 있는 문서에 상호 순위 점수를 할당합니다. 상호 순위 점수는 다음 공식을 사용하여 계산합니다.
1.0/{document position in the results + constant value}
그런 다음 쿼리는 각 문서에 대한 두 검색의 점수를 더하고, 합산된 점수를 기준으로 문서의 순위를 지정한 다음, 문서를 정렬하여 단일 결과를 반환합니다.
쿼리 변수
샘플 쿼리는 점수에 가중치를 추가하기 위해 다음 변수를 정의하며, 숫자가 작을수록 가중치가 높습니다.
vector_weightfull_text_weight
가중 상호 순위 점수는 다음 공식을 사용하여 계산합니다.
weight x reciprocal rank
쿼리 단계
샘플 쿼리는 다음 파이프라인 단계를 사용하여 컬렉션에 대한 시맨틱 검색을 수행하고 결과에서 문서의 상호 순위를 검색합니다.
쿼리의 | |
시맨틱 검색 결과의 모든 문서를 | |
| |
결과 내 각 문서에 대한 상호 순위 점수를 포함하는 | |
결과에 다음 필드만 포함합니다.
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샘플 쿼리는 $unionWith 단계를 사용하여 컬렉션에서 텍스트 검색을 수행하고 결과에서 문서의 상호 순위를 검색합니다.
| |
출력을 | |
전체 텍스트 검색에서 모든 문서를 | |
| |
결과 내 각 문서에 대한 상호 순위 점수를 포함하는 | |
결과에 다음 필드만 포함합니다.
|
샘플 쿼리는 다음 단계를 사용하여 시맨틱 및 텍스트 검색 결과를 결합하고 결과에서 단일 순위의 문서 목록을 반환합니다.
보면서 배우기
Atlas Search 전체 텍스트와 벡터 검색을 결합하여 하나의 병합된 결과 집합을 반환하는 하이브리드 검색 쿼리를 보여주는 애플리케이션 데모를 시청하세요. 이 애플리케이션은 RSF(상대 점수 통합) 및 RRF(역수 순위 통합)를 구현하여 순위 통합 알고리즘을 사용하여 생성된 병합된 세트를 반환합니다.
소요 시간: 2.43분