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하이브리드 검색 수행 방법

MongoDB Vector Search와 MongoDB Search 쿼리를 하이브리드 검색 으로 결합하여 통합된 결과를 얻을 수 있습니다.

이 튜토리얼은 영화에 대한 세부 정보를 포함하는 sample_mflix.embedded_movies 컬렉션에서 하이브리드 검색을 실행하는 방법을 보여줍니다. 구체적으로 이 튜토리얼은 다음 단계를 안내합니다.

  1. plot_embedding_voyage_4_large 필드에 MongoDB 벡터 검색 인덱스를 생성합니다. 이 필드에는 영화 줄거리 요약을 나타내는 벡터 임베딩(방문 Voyage AI의 voyage-4-large 임베딩 모델을 사용하여 생성된 2048 차원)이 포함되어 있습니다.

  2. sample_mflix.embedded_movies 컬렉션 에 MongoDB Search 인덱스 만듭니다.

  3. Run a query to combine the results from a $vectorSearch query and a $search query by using the following techniques:

    • 시맨틱 부스팅

    • Reciprocal Rank 퓨전

    • 점수 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

상대 점수 퓨전

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.

결과에서 문서의 스코어를 계산하여 다양한 검색 방법의 결과를 단일 순위 결과 세트로 결합합니다.

순위 지정 방법

  • Run $vectorSearch to retrieves semantically similar results.

  • Run $search to retrieve full-text results while boosting the documents that were returned by $vectorSearch.

  • Retrieves $vectorSearch and $search results.

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

  • Retrieves $vectorSearch and $search results.

  • 각 결과 세트에서 각 문서의 점수를 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

다음 공식을 사용하여 점수를 계산합니다.

score = w1 x score1 + w2 x score2

검색 기능

Supports further processing of results in subsequent aggregation stages such as $group.

Supports further processing of results in subsequent aggregation stages such as $group.

출력

다음의 문서를 포함하는 단일 결과 설정하다 $search

두 검색 방법의 문서가 모두 포함된 단일 결과 설정하다 .

두 검색 방법의 문서가 모두 포함된 단일 결과 설정하다 .

전제 조건

시작하기 전에 다음이 필요합니다.

  • MongoDB 버전 v8.0 이상이 설치된 Atlas cluster .

    참고

    Atlas cluster 에 MongoDB Search 인덱스와 MongoDB Vector Search 인덱스를 모두 저장 하고 성능이 뛰어난 쿼리를 실행 수 있는 충분한 메모리가 있는지 확인합니다.

  • The sample_mflix data loaded into your Atlas cluster.

  • mongosh Atlas cluster 에서 쿼리를 실행 .

  • Project Data Access Admin 프로젝트 에 액세스 MongoDB Vector Search 및 MongoDB Search 인덱스를 생성합니다.

MongoDB Vector Search 및 MongoDB Search 인덱스 만들기

이 섹션에서는 sample_mflix.embedded_movies collection의 필드에 다음 인덱스를 생성하는 방법을 보여 줍니다.

  • 해당 필드 에 대해 벡터 쿼리를 실행 위한 plot_embedding_voyage_4_large 필드 에 대한 MongoDB Vector Search 인덱스 .

  • 동적으로 인덱싱 가능한 모든 필드를 동적으로 인덱싱하여 해당 필드에 대한 전체 텍스트 검색을 실행하는 MongoDB Search 인덱스.

1

Connect to the cluster using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

인덱스를 생성하려는 컬렉션이 포함된 데이터베이스로 전환합니다.

use sample_mflix
switched to db sample_mflix
3

MongoDB Vector Search 인덱스 정의합니다.

다음 명령을 실행합니다. 이 인덱스 정의는 컬렉션 을 쿼리할 때 plot_embedding_voyage_4_large 필드 MongoDB Vector Search 필드 로 인덱싱합니다.

1db.embedded_movies.createSearchIndex(
2  "hybrid-vector-search", 
3  "vectorSearch", 
4  {
5    "fields": [
6      {
7        "type": "vector",
8        "path": "plot_embedding_voyage_4_large",
9        "numDimensions": 2048,
10        "similarity": "dotProduct"
11      }
12    ]
13  }
14);
4

MongoDB Search 인덱스 정의합니다.

다음 인덱스 정의는 컬렉션 에서 동적으로 인덱싱할 수 있는 모든 필드를 자동으로 인덱싱합니다.

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

하이브리드 검색 쿼리 실행

This section demonstrates how to query the data in the sample_mflix.embedded_movies collection for charming animal in the plot_embedding_voyage_4_large and fullplot fields by using the $vectorSearch and $search pipeline stages, respectively. The query enhances relevancy of $search queries by boosting the full-text scores using $vectorSearch scores for the same documents.

시맨틱 부스팅 쿼리

1

임베딩에 대한 파일 만듭니다.

  1. query-embeddings.js이라는 파일을 만듭니다.

    touch query-embeddings.js

  2. 다음 임베딩을 복사하여 파일 에 붙여넣습니다.

    CHARMING_ANIMAL_EMBEDDING=[-0.02300029993057251, -0.040718235075473785, 0.026521876454353333, -0.007813499309122562, 0.01958877220749855, -0.032134391367435455, 0.015186801552772522, 0.023990744724869728, -0.0029025499243289232, -0.0019671309273689985, 0.01254561822861433, -0.016287293285131454, -0.029053010046482086, 0.01408630795776844, -0.00891399197280407, 0.01958877220749855, -0.0508427694439888, -0.015737047418951988, 0.012215470895171165, -0.0036316262558102608, 0.0033152345567941666, 0.0033840155228972435, -0.00748335151001811, 0.002737476024776697, 0.03257458657026291, -0.01892847754061222, -0.012050396762788296, 0.02432089112699032, 0.028392715379595757, 0.033454980701208115, 0.005144803784787655, 0.0072632525116205215, 0.0212395116686821, -0.01683754101395607, -0.002751232124865055, 0.030813800171017647, -0.025861581787467003, 0.00748335151001811, -0.0032326977234333754, -0.030813800171017647, 0.006768031045794487, 0.024651039391756058, -0.03565596789121628, -0.02575153298676014, -0.005970173515379429, 0.02575153298676014, -0.08011587709188461, 0.0022284979932010174, -0.03257458657026291, 0.00522734085097909, -0.0028062567580491304, 0.011059952899813652, 0.0061627598479390144, 0.021349560469388962, -0.0061627598479390144, 0.02200985699892044, 0.006988129578530788, -0.006850568111985922, 0.010784829966723919, -0.0035490894224494696, -0.007538375910371542, 0.0023110350593924522, -0.031033897772431374, -0.04291922226548195, -0.03389517962932587, -0.004181872587651014, -0.02024906873703003, 0.003769187955185771, -0.01650739274919033, -0.0031226484570652246, -0.0026686950586736202, 0.03389517962932587, 0.020689265802502632, -0.025531433522701263, 0.02024906873703003, 0.03125399723649025, 0.006602956913411617, -0.01441645622253418, -0.04974227771162987, 0.005887636449187994, -0.013150889426469803, 0.007593400776386261, 0.00352157698944211, -0.014196357689797878, -0.009849410504102707, -0.019698821008205414, -0.01441645622253418, 0.0018708378775045276, -0.007703450042754412, 0.0020771801937371492, -0.03565596789121628, 0.027402272447943687, 0.007593400776386261, 0.005695050582289696, 0.03389517962932587, 0.014196357689797878, -0.002682451391592622, 0.030373603105545044, -0.01061975583434105, -0.017387786880135536, -0.00522734085097909, -0.013205913826823235, 0.007813499309122562, 0.02509123645722866, 0.00304011139087379, -0.04225892573595047, -0.011500149965286255, 0.005887636449187994, 0.0016163488617166877, -0.03477557376027107, 0.04423981159925461, -0.005144803784787655, -0.004401971120387316, -0.028062567114830017, 0.0087489178404212, 0.0029850867576897144, -0.0062177847139537334, -0.012215470895171165, -0.001430640695616603, 0.017277738079428673, -0.007813499309122562, -0.03191429376602173, 0.033454980701208115, 0.018818426877260208, 0.0035766016226261854, -0.030373603105545044, -0.056125134229660034, -0.01815813221037388, 0.0031501606572419405, 0.013756160624325275, -0.03785695508122444, -0.011775272898375988, 0.04908198118209839, 0.031474094837903976, -0.0004126848070882261, 0.027732418850064278, 0.01716768741607666, 0.005640025716274977, -0.013646110892295837, -0.010124534368515015, -0.005007242318242788, -0.02300029993057251, 0.027622370049357414, 0.015737047418951988, -0.03323488309979439, -0.00018312888278160244, -0.010509707033634186, -0.0024898650590330362, -0.014746603555977345, -0.004704606719315052, 0.03191429376602173, 0.02443094179034233, -0.0013756160624325275, -0.0046220701187849045, -0.049962375313043594, -0.01782798394560814, 0.0003920505696441978, -0.01892847754061222, 0.0012311763130128384, 0.041378531605005264, -0.041378531605005264, -0.02090936340391636, -0.010234583169221878, -0.008418770506978035, 0.03477557376027107, 0.009684337303042412, 0.05194326117634773, -0.010344632901251316, -0.047541290521621704, 0.010509707033634186, -0.03477557376027107, -0.023440496996045113, -0.007153203245252371, 0.002737476024776697, 0.020689265802502632, 0.020689265802502632, 0.03301478549838066, 0.012490593828260899, -0.024210842326283455, -0.0216797087341547, 0.005062267184257507, 0.0032877223566174507, -0.02949320711195469, -0.00232479115948081, -0.023990744724869728, 0.04534030333161354, 0.010124534368515015, 0.015516948886215687, 0.04490010812878609, -0.0007084422395564616, -0.04842168465256691, -0.025531433522701263, 0.0216797087341547, -0.010399657301604748, 0.049962375313043594, -0.02024906873703003, 0.03477557376027107, 0.008803942240774632, 0.02057921513915062, -0.01408630795776844, -0.06074720621109009, -0.010014484636485577, 0.034115277230739594, -0.017387786880135536, -0.02410079352557659, -0.01100492849946022, -0.02575153298676014, -0.026521876454353333, -0.03741675615310669, 0.011610199697315693, -0.045560404658317566, 0.0005811977898702025, 0.016067195683717728, 0.008473794907331467, -0.02024906873703003, -0.03961774334311485, 0.00748335151001811, 0.02784246951341629, 0.021349560469388962, 0.02333044819533825, 0.009574287571012974, 0.0006431004730984569, -0.035876065492630005, 0.002022155560553074, 0.019368674606084824, -0.004181872587651014, -0.011335075832903385, -0.02883291244506836, -0.000839125772472471, 0.006713006179779768, -0.00030263554072007537, -0.0025724018923938274, -0.013811185024678707, 0.001843325444497168, -0.013701136223971844, -0.017277738079428673, -0.006245296914130449, -0.006933104712516069, -0.014636554755270481, 0.0033977716229856014, 0.004044311121106148, -0.01584709621965885, 0.00858384370803833, -0.0042093852534890175, 0.0005124169983901083, -0.013315963558852673, -0.03301478549838066, -0.011059952899813652, -0.02057921513915062, 0.0010385900968685746, -0.002421084325760603, 0.00043503858614712954, 0.02443094179034233, 0.009794386103749275, -0.002751232124865055, 0.006382858380675316, 0.018488280475139618, 0.03565596789121628, -0.021789757534861565, 0.011830298230051994, -0.021569659933447838, -0.00034906258224509656, -0.032134391367435455, 0.015406900085508823, 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  3. 파일을 저장하고 닫습니다.

2

Connect to your cluster using mongosh.

Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect to a Cluster via mongosh.

3

sample_mflix 데이터베이스 사용합니다.

Run the following command at mongosh prompt:

use sample_mflix
switched to db sample_mflix
4

쿼리 에 사용할 임베딩을 로드합니다.

query-embeddings.js 파일 mongosh에 로드합니다.

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

embedded_movies 컬렉션에 대해 다음 MongoDB 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_voyage_4_large",
12            "queryVector": CHARMING_ANIMAL_EMBEDDING,
13            "numCandidates": numCandidates,   
14            "limit": numResults  
15        }  
16    },  
17    {  
18        $project: {  
19            "_id": 1,  
20            "title": 1,  
21            "fullplot": 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": "charming animal",  
48                            "path": "fullplot",
49                            "fuzzy": {}
50                        }  
51                    },  
52                    ...Object.keys(vectorScoresMap).map(id => ({  
53                        "equals": {  
54                            "path": "_id",  
55                            "value": id,  
56                            "score": { boost: { value: vectorScoresMap[id] } }  
57                        }  
58                    }))  
59                ]  
60            }  
61        }  
62    },  
63    {
64        $limit: 20
65    },
66    {  
67        $project: {  
68            "_id": 1,  
69            "title": 1,  
70            "fullplot": 1,
71            "score": { $meta: "searchScore" }  
72        }  
73    }  
74]).toArray();

쿼리 정보

The sample query boosts the $search results based on $vectorSearch result scores to return the most relevant documents. For semantic boosting, you perform the following steps:

  1. Run the $vectorSearch query.

    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 쿼리

You can run a query that retrieves the sorted search results from the semantic search and the full-text search, and assigns a reciprocal rank score to the documents in the results based on their position in the results array. To learn more about the reciprocal rank fusion technique and how to run a reciprocal rank fusion query, see Perform Hybrid Vector and Full-Text Search.

상대 점수 퓨전 쿼리

You can run a query that retrieves the scored search results from the semantic and full-text searches and applies the sigmoid expression to normalize the score before combining the results from the pipelines into a single aggregated score for each document. To learn more about the score fusion technique and how to run a score fusion query, see Perform Hybrid Vector and Full-Text Search.

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