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하이브리드 검색 수행 방법

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.

  1. plot_embedding 필드에 Atlas Vector Search 인덱스를 생성합니다. 이 필드에는 영화 줄거리 요약을 나타내는 벡터 임베딩이 포함되어 있습니다.

  2. sample_mflix.embedded_movies 컬렉션에 Atlas Search 인덱스를 만듭니다.

  3. 다음 기술을 사용하여 쿼리 실행하여 $vectorSearch 쿼리와 $search 쿼리의 결과를 결합합니다.

    • 시맨틱 부스팅

    • Reciprocal Rank 퓨전

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.

다음 기술을 적용 하이브리드 검색 달성할 수 있습니다.

테크닉

시맨틱 부스팅

상호 순위 통합

목적

벡터 검색 과 일치하는 문서를 강화하여 전체 텍스트 검색 결과의 정확성을 개선합니다. 매우 큰 결과 세트를 검색할 때 유용합니다.

결과에서 문서의 상호 순위를 계산하여 다양한 검색 방법의 결과를 순위가 설정하다 단일 결과 세트로 결합합니다.

순위 지정 방법

  • $vectorSearch 를 실행하여 의미적으로 유사한 결과를 조회합니다.

  • $search 를 실행하여 $vectorSearch에서 반환된 문서를 부스트하면서 전체 텍스트 결과를 조회.

  • $vectorSearch 및 $search 결과를 검색합니다.

  • 각 결과 세트에서 각 문서 의 순위를 사용하여 두 결과 세트를 모두 병합합니다.

채점 방법

벡터 검색 의 결과와 일치하는 문서에 대한 전체 텍스트 결과 점수를 가중치 벡터 검색 점수만큼 높입니다.

다음 공식을 사용하여 점수를 계산합니다.

1.0/{document position in the results + constant value}

그런 다음 각 검색 방법에 대해 서로 다른 가중치(w)를 적용하여 해당 검색 방법에 더 많은 중요성을 부여합니다. 각 문서 에 대한 가중치 역수 순위는 가중치에 문서 의 역수 순위를 곱하여 계산됩니다.

weighted_reciprocal_rank = w x reciprocal_rank

검색 기능

$group와 같은 후속 집계 단계에서 결과의 추가 처리 지원합니다.

출력

다음의 문서를 포함하는 단일 결과 설정하다 $search

두 검색 방법의 문서가 모두 포함된 단일 결과 설정하다 .

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 cluster 에 로드된 sample_mflix 데이터입니다.

  • mongosh Atlas cluster 에서 쿼리를 실행 .

  • Project Data Access Admin 의 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_embedding 필드에 대한 Atlas Vector Search 인덱스입니다.

  • 해당 필드에 대해 전체 텍스트 검색을 실행하기 위한 title 필드에 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.

  1. mongosh를 사용하여 Atlas 클러스터에 연결합니다.

    자세한 내용은 mongosh를 통한 연결을 참조하세요.

  2. 인덱스를 생성하려는 컬렉션이 포함된 데이터베이스로 전환합니다.

    use sample_mflix
    switched to db sample_mflix

  3. db.collection.createSearchIndex() 메서드를 실행합니다.

    1db.embedded_movies.createSearchIndex(
    2  "hybrid-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 컬렉션 에서 동적으로 인덱싱할 수 있는 모든 필드를 자동으로 인덱싱합니다.

mongosh v2.1.2 이상을 사용하여 컬렉션의 Atlas Search 인덱스를 만들려면 다음 단계를 수행하세요.

  1. mongosh를 사용하여 Atlas 클러스터에 연결합니다.

    자세한 내용은 mongosh를 통한 연결을 참조하세요.

  2. 인덱스를 생성하려는 컬렉션이 포함된 데이터베이스로 전환합니다.

    use sample_mflix
    switched to db sample_mflix

  3. db.collection.createSearchIndex() 메서드를 실행합니다.

    1db.embedded_movies.createSearchIndex(
    2  "hybrid-full-text-search", 
    3  "search", 
    4  {
    5    "mappings": {
    6      "dynamic": true
    7    }
    8  }
    9);

시맨틱 부스팅

이 섹션에서는 sample_mflix.embedded_movies plot_embedding plot $vectorSearch 및 $search 파이프라인 단계를 각각 사용하여 및 필드의 스타워즈에 대한 컬렉션 의 데이터를 쿼리 방법을 보여 줍니다. 이 쿼리 동일한 문서에 $search $vectorSearch 대해 점수를 사용하여 전체 텍스트 점수를 높여 쿼리의 관련성을 높입니다.

절차

1

임베딩에 대한 파일 만듭니다.

  1. query-embeddings.js이라는 파일을 만듭니다.

    touch query-embeddings.js

  2. 다음 임베딩을 복사하여 파일 에 붙여넣습니다.

    QUERY_EMBEDDING = 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  3. 파일을 저장하고 닫습니다.

2

mongosh를 사용하여 클러스터에 연결합니다.

터미널 창에서 mongosh 를 열고 클러스터에 연결합니다. 연결에 대한 자세한 지침은 mongosh 를 통한 연결을 참조하세요.

3

sample_mflix 데이터베이스를 사용합니다.

mongosh 프롬프트에서 다음 명령을 실행합니다.

use sample_mflix
switched to db sample_mflix
4

쿼리 에 사용할 임베딩을 로드합니다.

query-embeddings.js 파일 mongosh에 로드합니다.

load('<path-to-query-embeddings.js-file>');
5

embedded_movies 컬렉션에 대해 다음 Atlas Search 쿼리를 실행합니다.

1vectorWeight = 1;  
2vectorCutoff = 0.7; 
3numResults = 20;
4numCandidates = numResults * 10;  
5
6// Execute the vector search  
7vectorResults = db.embedded_movies.aggregate([  
8    {  
9        $vectorSearch: {  
10            index: "hybrid-vector-search", 
11            path: "plot_embedding",
12            queryVector: QUERY_EMBEDDING,   
13            numCandidates: numCandidates,   
14            limit: numResults  
15        }  
16    },  
17    {  
18        $project: {  
19            _id: 1,  
20            title: 1,  
21            plot: 1,
22            vectorScore: { $meta: "vectorSearchScore" }  
23        }  
24    },  
25    {  
26        $match: {  
27            vectorScore: { $gte: vectorCutoff }  
28        }  
29    }  
30]).toArray();  
31
32// Create a map for fast lookup  
33vectorScoresMap = {};  
34vectorResults.forEach(result => {  
35    vectorScoresMap[result._id] = result.vectorScore * vectorWeight;  
36});  
37
38// Execute the text search with boosting based just on ID  
39textResults = db.embedded_movies.aggregate([  
40    {  
41        $search: {  
42            index: "hybrid-full-text-search",  
43            compound: {  
44                should: [  
45                    {  
46                        text: {  
47                            query: "star wars",  
48                            path: "plot"  
49                        }  
50                    },  
51                    ...Object.keys(vectorScoresMap).map(id => ({  
52                        equals: {  
53                            path: "_id",  
54                            value: id,  
55                            score: { boost: { value: vectorScoresMap[id] } }  
56                        }  
57                    }))  
58                ]  
59            }  
60        }  
61    },  
62    {
63        $limit: 20
64    },
65    {  
66        $project: {  
67            _id: 1,  
68            title: 1,  
69            plot: 1,
70            score: { $meta: "searchScore" }  
71        }  
72    }  
73]).toArray();  
[
  {
    _id: ObjectId('573a139df29313caabcfa90b'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.401333808898926
  },
  {
    _id: ObjectId('573a1397f29313caabce6f53'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.401333808898926
  },
  {
    _id: ObjectId('573a1398f29313caabce9851'),
    plot: 'Johnathan Cabot is a champion gymnast. In the tiny, yet savage, country of Parmistan, there is a perfect spot for a "star wars" site. For the US to get this site, they must compete in the ...',
    title: 'Gymkata',
    score: 4.284864902496338
  },
  {
    _id: ObjectId('573a1394f29313caabcdf65b'),
    plot: 'A fantastic tale of war, love, family and ambition set in the midst of the Japanese Civil Wars of the sixteenth century.',
    title: 'Ugetsu',
    score: 2.858368396759033
  },
  {
    _id: ObjectId('573a13a4f29313caabd1137f'),
    plot: "A producer's film is endangered when his star walks off, so he decides to digitally create an actress to substitute for the star, becoming an overnight sensation that everyone thinks is a real person.",
    title: 'S1m0ne',
    score: 2.842216730117798
  },
  {
    _id: ObjectId('573a139af29313caabcefe79'),
    plot: 'An all star baseball player becomes the unhealthy focus of a down on his luck salesman.',
    title: 'The Fan',
    score: 2.6598150730133057
  },
  {
    _id: ObjectId('573a13a3f29313caabd0c992'),
    plot: 'The vampire Lestat becomes a rock star whose music wakes up the queen of all vampires.',
    title: 'Queen of the Damned',
    score: 2.6598150730133057
  },
  {
    _id: ObjectId('573a13a8f29313caabd1da86'),
    plot: 'During the Napoleonic Wars, a brash British captain pushes his ship and crew to their limits in pursuit of a formidable French war vessel around South America.',
    title: 'Master and Commander: The Far Side of the World',
    score: 2.643709897994995
  },
  {
    _id: ObjectId('573a139af29313caabcf0e7d'),
    plot: 'A puppeteer discovers a portal that leads literally into the head of the movie star, John Malkovich.',
    title: 'Being John Malkovich',
    score: 2.612804889678955
  },
  {
    _id: ObjectId('573a13b6f29313caabd4614f'),
    plot: 'A former pop star who now writes commercial jingles for a living experiences a mid-life crisis.',
    title: 'The Good Night',
    score: 2.612804889678955
  },
  {
    _id: ObjectId('573a13cbf29313caabd7fbc9'),
    plot: 'Bertrand Tavernier is in top form with this gripping, superbly mounted drama set against the savage Catholic/Protestant wars that ripped France apart in the 16th century. Based on a novella...',
    title: 'The Princess of Montpensier',
    score: 2.4938809871673584
  },
  {
    _id: ObjectId('573a139bf29313caabcf2d8e'),
    plot: 'Updated version of the classic Rodgers and Hammerstein musical of the classic fairy-tale, with an all-star, multi-racial cast.',
    title: 'Cinderella',
    score: 2.44028377532959
  },
  {
    _id: ObjectId('573a1399f29313caabcec853'),
    plot: 'An action movie star researching a role is allowed to tag along with a hardboiled New York policeman, who finds him superficial and irritating.',
    title: 'The Hard Way',
    score: 2.325138807296753
  },
  {
    _id: ObjectId('573a13b4f29313caabd3f550'),
    plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.',
    title: 'Divergence',
    score: 2.325138807296753
  },
  {
    _id: ObjectId('573a13aff29313caabd2fe20'),
    plot: 'An intimate story set during the 1860s in which a young Irish woman Sarah and her family find themselves on both sides of the turbulent wars between British and Maori during the British colonization of New Zealand.',
    title: 'River Queen',
    score: 2.298485040664673
  },
  {
    _id: ObjectId('573a13aef29313caabd2df95'),
    plot: 'Story of two sisters that grew up in a small Serbian village in the beginning of the 1930s. The village is torn up by wars and years long blood oath. There are no men left in the village. ...',
    title: 'Tears for Sale',
    score: 2.268857479095459
  },
  {
    _id: ObjectId('573a13b8f29313caabd4d8e1'),
    plot: "Thomas Michael, Paolo Mancini, Chris Klein and Joe Mantegna star in director Matthiew Klinck's holiday-themed comedy concerning two downsized Easter bunnies whose friendship starts to ...",
    title: 'Hank and Mike',
    score: 2.254227876663208
  },
  {
    _id: ObjectId('573a139af29313caabcf124d'),
    plot: 'As the Clone Wars near an end, the Sith Lord Darth Sidious steps out of the shadows, at which time Anakin succumbs to his emotions, becoming Darth Vader and putting his relationships with Obi-Wan and Padme at risk.',
    title: 'Star Wars: Episode III - Revenge of the Sith',
    score: 2.2399840354919434
  },
  {
    _id: ObjectId('573a13c1f29313caabd633ac'),
    plot: "Black Dynamite is the greatest African-American action star of the 1970s. When his only brother is killed by The Man it's up to him to find justice.",
    title: 'Black Dynamite',
    score: 2.1875147819519043
  },
  {
    _id: ObjectId('573a13dbf29313caabdaf598'),
    plot: "Just as Clint Eastwood's star-making spaghetti Western A Fistful of Dollars was inspired by Akira Kurosawa's Yojimbo, Japanese-Korean filmmaker Sang-il Lee (Villain) has decided to ...",
    title: 'Unforgiven',
    score: 2.1875147819519043
  }
]

쿼리 정보

샘플 쿼리 결과 $search $vectorSearch 점수를 기반으로 결과를 부스트하여 가장 관련성이 높은 문서를 반환합니다. 시맨틱 부스팅의 경우 다음 단계를 수행합니다.

  1. $vectorSearch 쿼리 실행합니다.

    초과 요청 값을 사용하여 고려해야 할 지정된 제한보다 많은 후보 수를 지정합니다. 초과 요청 사용하면 다음을 수행할 수 있습니다.

    • 초기 검색 에서 고려할 수 있는 많은 수의 광범위한 문서 풀을 포함합니다.

    • 후속 단계에서 점수 계산을 개선합니다.

    이 쿼리 다음 계산을 사용하여 벡터 검색 중에 고려해야 할 후보자 수를 결정합니다.

    numCandidates = limit * over request

    결과는 최소 점수 임계값(vectorCutoff)을 기준으로 필터링됩니다.

  2. 벡터 검색 점수를 조정하여 벡터 검색 결과가 문서의 점수 및 순위에 미치는 영향을 조정합니다.

    vectorWeight 매개변수를 사용하여 가중치를 설정하다 하고 벡터 검색 점수에 vectorWeight을 곱합니다. 이는 벡터 검색 결과의 중요도를 제어하고 의미적으로 가장 관련성이 높은 문서만 고려되도록 합니다.

  3. $search 쿼리 실행하고 벡터 검색 결과와 일치하는 문서의 점수를 높입니다.

상호 순위 통합

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

쿼리 에 사용할 임베딩을 설정합니다.

  1. query-embeddings.js이라는 파일을 만듭니다.

  2. 다음 임베딩을 복사하여 파일 에 붙여넣습니다.

  3. 파일을 저장하고 닫습니다.

2

mongosh를 사용하여 클러스터에 연결합니다.

터미널 창에서 mongosh 를 열고 클러스터에 연결합니다. 연결에 대한 자세한 지침은 mongosh 를 통한 연결을 참조하세요.

3

sample_mflix 데이터베이스를 사용합니다.

mongosh 프롬프트에서 다음 명령을 실행합니다.

use sample_mflix
switched to db sample_mflix
4

쿼리 에 사용할 임베딩을 로드합니다.

다음 명령을 실행하여 임베딩을 로드합니다.

load('<path-to-query-embeddings.js');

임베딩이 성공적으로 로드되었는지 확인하려면 다음 명령을 실행 .

STAR_WARS_EMBEDDING.length
1536
5

embedded_movies 컬렉션 에 대해 다음 Atlas Search 및 Atlas Vector Search 쿼리 실행합니다.

1var vector_weight = 0.1;
2var full_text_weight = 0.9;
3db.embedded_movies.aggregate([
4  {
5    "$vectorSearch": {
6      "index": "hybrid-vector-search",
7      "path": "plot_embedding",
8      "queryVector": 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67        }, {
68          "$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
}

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.

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

돌아가기

점수 산정 사용자 지정

다음

동의어

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