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

    • 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

  • $vectorSearch 를 실행하여 의미적으로 유사한 결과를 조회합니다.

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

  • $vectorSearch 및 $search 결과를 조회합니다.

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

  • $vectorSearch 및 $search 결과를 조회합니다.

  • 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

$group와 같은 후속 집계 단계에서 결과의 추가 처리 지원합니다.

$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 .

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.

  • Atlas cluster에 로드된 sample_mflix 데이터입니다.

  • 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

mongosh을(를) 사용하여 클러스터에 연결합니다.

To learn more, see mongosh 통해 클러스터에 연결을참조하세요.

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

이 섹션에서는 sample_mflix.embedded_movies plot_embedding_voyage_4_large fullplot 및 파이프라인 단계를 $vectorSearch 사용하여 및 필드의 charming $search animal 에 대핕 컬렉션의 데이터를 쿼리하는 방법을 보여줍니다. 쿼리는 동일한 문서에 $vectorSearch 대한 점수를 사용하여 전체 텍스트 $search 점수를 부스팅하여 쿼리의 유용성을 향상시킵니다.

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, 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  3. 파일을 저장하고 닫습니다.

2

mongosh을(를) 사용하여 클러스터에 연결합니다.

터미널 창에서 mongosh 을 열고 클러스터에 연결합니다. 연결에 대한 자세한 지침은 mongosh 통해 클러스터에 연결을참조하세요.

3

Use the sample_mflix database.

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-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();  
[
  {
    _id: ObjectId('573a13a3f29313caabd0d0a8'),
    fullplot: "Dr. John Dolittle the beloved doctor is back, but this time around he plays cupid to bumbling circus bear Archie as he's so smitten by a Pacific Western bear female, Ava. Dr. Dolittle must help a group of forest creatures to save their forest. But with the aid of his mangy, madcap animal friends, Dr. Dolittle must teach Archie the ways of true romance in time to save his species and his home before their habit is gone. So John held a meeting for every animal in the forest to not give up without a fight no matter what kind of animal expression they have and everyone agrees to do it and save their home.",
    title: 'Dr. Dolittle 2',
    score: 3.2186570167541504
  },
  {
    _id: ObjectId('573a13c5f29313caabd71215'),
    title: 'The Bannen Way',
    fullplot: 'A slick, sexy, action-packed show about Neal Bannen, a charming con-man with a police chief for a father, a mob boss for an uncle and a weakness for beautiful women, who wants to turn his life around and leave the criminal lifestyle forever.',
    score: 3.066267967224121
  },
  {
    _id: ObjectId('573a1394f29313caabce074d'),
    title: 'Perri',
    fullplot: 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten, Perri finds time to fall in love with her prince-charming male squirrel.',
    score: 3.0323688983917236
  },
  {
    _id: ObjectId('573a139af29313caabcf0e1d'),
    title: 'Wild America',
    fullplot: 'Three brothers - Marshall, Marty and Mark dream of becoming naturalists and portraying animal life of America. One summer their dream comes true, they travel through America, filming alligators, bears and moose.',
    score: 2.9388742446899414
  },
  {
    _id: ObjectId('573a13e3f29313caabdc08f2'),
    title: 'Bang Bang',
    fullplot: "A chance encounter of the unassuming bank receptionist Harleen Sahni with the charming yet mysterious Rajveer Nanda, results in an on-rush of ditched planes, car chases, shoot-outs, bombing raids and general global mayhem. But as the transcontinental chase ensues with Rajveer convincing Harleen that he's the good guy, can she really trust him, and will trust matter when the bullets start flying?",
    score: 2.7579541206359863
  },
  {
    _id: ObjectId('573a1393f29313caabcdbe7c'),
    fullplot: "In this charming film based on the popular L. Frank Baum stories, Dorothy and her dog Toto are caught in a tornado's path and somehow end up in the land of Oz. Here she meets some memorable friends and foes in her journey to meet the Wizard of Oz who everyone says can help her return home and possibly grant her new friends their goals of a brain, heart and courage.",
    title: 'The Wizard of Oz',
    score: 2.703585147857666
  },
  {
    _id: ObjectId('573a1397f29313caabce80f6'),
    title: 'Gauche the Cellist',
    fullplot: 'A cellist in a small orchestra receives help from animals to help him practice his music.',
    score: 2.692498207092285
  },
  {
    _id: ObjectId('573a13bcf29313caabd55f51'),
    title: 'The Missing Lynx',
    fullplot: "A group of animals look for a way off of an eccentric billionaire's own personal Noah's Ark.",
    score: 2.6747734546661377
  },
  {
    _id: ObjectId('573a1397f29313caabce8aac'),
    title: 'The Grey Fox',
    fullplot: `Old West highwayman Bill Miner, known to Pinkertons as "The Gentleman Bandit," is released in 1901 after 33 years in prison, a genial and charming old man. He goes to Washington to live and work with his sister's family. But the world has changed much while he has been away, and he just can't adjust. So he goes to Canada and returns to the only thing familiar to him -- robbery (with stagecoaches changed to trains).`,
    score: 2.651318073272705
  },
  {
    _id: ObjectId('573a13c7f29313caabd75624'),
    fullplot: 'Extraordinary teen John Smith (Pettyfer) is a fugitive on the run from ruthless enemies sent to destroy him. Changing his identity, moving from town to town with his guardian Henri (Olyphant), John is always the new kid with no ties to his past. In the small Ohio town he now calls home, John encounters unexpected, life-changing events-his first love (Agron), powerful new abilities and a connection to the others who share his incredible destiny.',
    title: 'I Am Number Four',
    score: 2.6149134635925293
  },
  {
    _id: ObjectId('573a1392f29313caabcdb48b'),
    title: 'Snow White and the Seven Dwarfs',
    fullplot: 'The first, and by far most memorable full-length animated feature from the Disney Studios, "Snow White and the Seven Dwarfs" may have been superseded technically by many of the films that followed it. But its simple story of a charming little princess saved from the evil deeds of her wicked stepmother, the queen, by a group of seven adorable dwarfs made history when it was first released in December, 1937 and has since become an incomparable screen classic.',
    score: 2.6010334491729736
  },
  {
    _id: ObjectId('573a13b4f29313caabd4070d'),
    title: 'Paragraph 78',
    fullplot: 'This is a wonderful movie about a group of men frontier the Nashville area who like to make puppets out of real animals',
    score: 2.573139190673828
  },
  {
    _id: ObjectId('573a1393f29313caabcdc6e9'),
    title: 'Tom Dick and Harry',
    fullplot: "Working girl/telephone operator Janie is engaged to conservative, dull, but very reliable car salesman Tom, who offers her a safe, stable marriage. Then she meets unconventional slacker Harry, a philosophical car mechanic without much ambition although she does hear bells when they kiss. Through Harry, the fickle Janie meets Dick, a handsome and charming millionaire playboy, who embodies her greatest romantic fantasies, and after a champagne-filled overnight flight to Chicago, she thinks she's met her dream man, but has she?",
    score: 2.5526208877563477
  },
  {
    _id: ObjectId('573a13acf29313caabd28039'),
    title: 'The Science of Sleep',
    fullplot: 'Following the death of his father in Mexico, Stèphane Miroux, a shy insecure young man, agrees to come to Paris to draw closer to his widowed mother Christine. He lands a boring job at a calendar-making firm and falls in love with his charming neighbor Stèphanie. But conquering her is no bed of roses for the young man and the only solution he finds to put up with the difficulties he is going through is escape into a dream world...',
    score: 2.5526208877563477
  },
  {
    _id: ObjectId('573a13b3f29313caabd3b4e1'),
    fullplot: `Manny the woolly mammoth, Sid the sloth, Diego the saber-toothed tiger, and the hapless prehistoric squirrel/rat known as Scrat are still together and enjoying the perks of their now melting world. Manny may be ready to start a family, but nobody has seen another mammoth for a long time; Manny thinks he may be the last one. That is, until he miraculously finds Ellie, the only female mammoth left in the world. Their only problems: They can't stand each other--and Ellie somehow thinks she's a possum! Ellie comes with some excess baggage in the form of her two possum "brothers"-- Crash and Eddie, a couple of daredevil pranksters and cocky, loud-mouthed troublemakers. Manny, Sid and Diego quickly learn that the warming climate has one major drawback: A huge glacial dam holding off oceans of water is about to break, threatening the entire valley. The only chance of survival lies at the other end of the valley. So our three heroes, along with Ellie, Crash and Eddie, form the most unlikely family--in any "Age"-- as they embark on a mission across an ever-changing, increasingly dangerous landscape towards their salvation.`,
    title: 'Ice Age: The Meltdown',
    score: 2.518824338912964
  },
  {
    _id: ObjectId('573a1395f29313caabce1b5f'),
    title: 'Hatari!',
    fullplot: `John Wayne and his ensemble cast cavort over the African landscape filling orders from zoos for wild animals. Bruce Cabot plays "the Indian", a womanizing sharpshooter who is gored by a rhino in the opening scenes of the film. This becomes a running theme through the movie; their bad luck in catching rhinos, and provides the climactic ending chase. While Bruce is in the hospital, Elsa Martinelli shows up as a woman photographer from a Swiss zoo, and John wants to send her packing. She strongarms the Duke into letting her stay by promising that her zoo will buy most of their animals this season if she's allowed to go along on the hunts and take photos. Hardy Kruger, Gerard Blain, Michelle Girardon and Valentin de Vargas round out the group. They traipse over the African landscape capturing animals; Elsa also has a running gag where she collects baby elephants as the movie goes along. In the end she's acquired three of them.`,
    score: 2.4652297496795654
  },
  {
    _id: ObjectId('573a13c5f29313caabd6e7a4'),
    title: 'Merantau',
    fullplot: `In Minangkabau, West Sumatera, Yuda a skilled practitioner of Silat Harimau is in the final preparations to begin his "Merantau" a century's old rites-of-passage to be carried out by the community's young men that will see him leave the comforts of his idyllic farming village and make a name for himself in the bustling city of Jakarta. After a series of setbacks leave Yuda homeless and uncertain about his new future, a chance encounter results in him defending the orphaned Astri from becoming the latest victim of a European human trafficking ring led by the wildly psychotic, Ratger and his right-hand man Lars. With Ratger injured in the mèlèe and seeking both his "merchandise" and bloody retribution, Yuda's introduction to this bustling city is a baptism of fire as he is forced to go on the run with Astri and her younger brother Adit as all the pimps and gangsters that inhabit the night hound the streets chasing their every step. With escape seemingly beyond their grasp, Yuda has no choice but to face his attackers in an adrenaline charged, jaw-dropping finale.`,
    score: 2.4573898315429688
  },
  {
    _id: ObjectId('573a1398f29313caabceaddd'),
    title: 'Dead Heat',
    fullplot: 'Roger Mortis and Doug Bigelow are cops that are chasing crooks that are dead serious about crime. Or should I say they are chasing dead crooks perpetrating serious crimes? Seems some nutcase has learned how to bring back the dead and is sending them on crime sprees. Now these indestructable goons are in the way of officers Mortis and Bigelow. To even things up, when Mortis is killed (in the line of duty, of course) he gets a jump start from the Resurrection machine and takes the fight to the zombie bad guys.',
    score: 2.398913860321045
  },
  {
    _id: ObjectId('573a1393f29313caabcde3af'),
    title: 'Mighty Joe Young',
    fullplot: `In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to Africa with the Oklahoma cowboy Gregg to bring attractions to his new night-club in Hollywood. They capture several lions and out of blue, they see a huge gorilla nearby their camping and they try to capture the animal. However, the teenager Jill Young stops the men that intended to kill her gorilla. Max seduces Jill with a fancy life in Hollywood and she signs a contract with him where the gorilla Joseph "Joe" Young would be the lead attraction. Soon she realizes that her dream is a nightmare to Joe and she asks Max to return to Africa. However he persuades her to stay a little longer in the show business. But when three alcoholic costumers give booze to Joe, the gorilla destroys the spot and is sentenced by the justice to be sacrificed. Will Jill, Gregg and Max succeed in saving Joe?`,
    score: 2.3978404998779297
  },
  {
    _id: ObjectId('573a13c7f29313caabd75592'),
    fullplot: 'FBI agent Malcolm Turner and his son, Trent, go undercover at an all-girls performing arts school after Trent witnesses a murder. Posing as Big Momma and Charmaine, they must find the murderer before he finds them.',
    title: 'Big Mommas: Like Father, Like Son',
    score: 2.3933444023132324
  }
]

쿼리 정보

샘플 쿼리 $search 결과를 $vectorSearch 결과 점수를 기반으로 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.

쿼리 변수

쿼리 에 사용된 변수입니다.

샘플 쿼리 다음 변수를 정의합니다.

  • vectorWeight 벡터 검색 점수에 가중치를 추가하며, 숫자가 낮을수록 가중치가 높습니다.

  • vectorCutoff 부스트할 결과에 대한 최소 벡터 점수를 설정하다 .

  • numResults 원하는 검색 결과 수 지정

  • numCandidates 초기 검색 시 고려할 후보 수를 계산합니다. 다음 공식을 사용하여 후보자 수를 계산합니다.

    numCandidates = numResults x overRequestFactor
쿼리 단계

쿼리 에 사용된 집계 파이프라인 단계입니다.

샘플 쿼리는 다음 파이프라인 단계를 사용하여 컬렉션에 대한 시맨틱 검색을 수행하고 결과에서 문서의 상호 순위를 검색합니다.

$vectorSearch

쿼리의 queryVector 필드에 벡터 임벡딩으로 지정된 string charming animal을 plot_embedding_voyage_4_large 필드에서 검색합니다. 이 쿼리는 Voyage AI의 voyage-4-large 임베딩 모델을 사용하며, 이는 plot_embedding_voyage_4_large 필드의 임베딩에 사용된 모델과 동일합니다.

$search

fullplot 필드에 charming animal이라는 터미 포함된 영화를 검색합니다. 벡터 검색 결과의 _id 에 일치하는 결과의 문서에 대해 쿼리는 문서의 조정된 벡터 검색 점수로 점수를 부스트합니다.

상호 순위 통합 쿼리

시맨틱 검색과 전체 텍스트 검색에서 정렬된 검색 결과를 조회하고, 결과 배열에서의 위치에 따라 결과에 있는 문서에 상호 순위 점수를 할당하는 쿼리를 실행할 수 있습니다. 상호 순위 합치 기법과 상호 순위 합치 쿼리를 실행하는 방법에 대해 자세히 알아보려면 하이브리드 벡터 및 전체 텍스트 검색 수행을 참조하세요.

상대 점수 퓨전 쿼리

시맨틱 및 전체 텍스트 검색에서 점수가 매겼진 검색 결과를 조회하고 파이프라인의 결과를 결합하기 전에 sigmoid 표현식을 적용하여 점수를 정규화하는 쿼리를 실행할 수 있습니다. 그리고 각 문서에 대한 단일 집계 점수로 파이프라인의 점수를 결합합니다. 점수 퓨전 기법과 점수 퓨전 쿼리 실행 방법에 대해 자세히 알아보려면 하이브리드 벡터 및 전체 텍스트 검색 수행을 참조하십시오.

돌아가기

점수 산정 사용자 지정

다음

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