여러 `$vectorSearch` 쿼리를 결합하는 방법

The $rankFusion and $scoreFusion stages are available as Preview features. To learn more, see Preview Features.

Starting in v8.1, the MongoDB $rankFusion aggregation stage supports $vectorSearch inside the input pipeline. You can use $rankFusion to combine multiple $vectorSearch queries against the same collection in the same pipeline. $rankFusion first executes all input pipelines independently, and then de-duplicates and combines the input pipeline results into a final ranked results set. To run multiple $vectorSearch queries against multiple collections in a single pipeline, use $unionWith.

사용 사례 및 이점

$rankFusion 파이프라인을 사용하면 다음 유형의 $vectorSearch 쿼리를 실행할 수 있습니다.

동일한 필드에서 유사한 용어에 대해 여러 $vectorSearch 쿼리를 실행합니다.
이 기능은 동일한 쿼리 내에서 데이터셋을 검색하여 유사한 용어를 포괄적으로 검색할 수 있도록 합니다.
여러 필드에 대해 동일한 용어로 $vectorSearch 쿼리를 실행합니다.
이 기능을 사용하면 데이터셋의 여러 필드를 검색하여 쿼리에 가장 적합한 결과를 반환하는 필드를 확인할 수 있습니다.
동일한 텀에 대해 서로 다른 임베딩 모델의 임베딩을 대상으로 여러 $vectorSearch 쿼리를 실행합니다.
이 기능을 사용하면 다양한 임베딩 모델의 임베딩을 검색하여 서로 다른 모델 간의 의미 해석 차이를 파악할 수 있습니다.

튜토리얼 소개

이 튜토리얼은 sample_mflix 데이터베이스의 embedded_movies 컬렉션에 대해 다양한 $vectorSearch 쿼리를 실행하는 방법을 설명합니다. 이 컬렉션에는 다음 필드가 포함됩니다.

plot_embeddingtext-embedding-ada-002 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다.
plot_embedding_voyage_3_large, Voyage AI의 voyage-3-large 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다. 이 튜토리얼의 모든 샘플 쿼리에 이 필드를 사용합니다.

사용 사례 및 이점에 나열된 두 번째 쿼리에 대해 다른 필드에 대한 임베딩을 생성해야 합니다. 이 사용 사례에 대한 샘플 쿼리를 시도하려면 임베딩 생성단계를 완료하세요.

이 튜토리얼에서는 다음 단계를 안내합니다.

Set up an MongoDB Vector Search index on the sample_mflix.embedded_movies collection.
다음 검색을 수행하기 위해 $rankFusion과 함께 $vectorSearch 쿼리를 실행합니다.
- plot_embedding_voyage_3_large 필드에서 light-hearted comedy with ghosts 및 slapstick humor with paranormal events와 비슷한 줄거리를 가진 영화를 검색하세요.
- Voyage AI의 임베딩을 사용하여 plot_embedding_voyage_3_large 및 title_embedding 필드에서 battle between good and evil과 유사한 플롯을 가진 영화를 검색합니다.
- OpenAI와 Voyage AI의 임베딩을 사용하여 plot_embedding 및 plot_embedding_voyage_3_large 필드에서 journey across lands와 유사한 줄거리를 가진 영화를 검색하세요.

시작하기 전에 Atlas 클러스터가 필수구성 요소에 설명된 요건을 충족하는지 확인하십시오.

참고

$rankFusion과 함께 $vectorSearch 쿼리를 실행하려면, 클러스터가 v8.1 이상을 실행해야 합니다.

임베딩 생성

여러 필드에 대해 동일한 텀을 쿼리하려면, 서로 다른 필드에 대한 임베딩을 포함하도록 데이터 세트를 준비해야 합니다. 이 섹션에서는 Voyage AI의 voyage-3-large 임베딩 모델을 사용하여 컬렉션의 title 필드에 대한 임베딩을 생성하는 방법을 설명합니다.

다른 샘플 사용 사례만 시도하고 싶다면 이 섹션을 건너뛸 수 있습니다.

전제 조건

VS Code 또는 Colab과 같은 대화형 Python 노트북을 실행 위한 환경입니다.
Voyage AI 임베딩 모델용 API 키.

절차

이 섹션에서는 Voyage AI의 voyage-3-large 임베딩 모델을 사용하여 embedded_movies 컬렉션의 title 필드에 대한 임베딩을 생성합니다.

환경을 설정합니다.

.ipynb 확장자를 가진 파일을 저장하여 대화형 Python 노트북을 만든 다음 노트북에서 다음 명령을 실행하여 종속성을 설치합니다.

pip install --quiet voyageai pymongo

벡터 임베딩을 생성하는 함수를 정의하고 사용합니다.

다음의 자리 표시자 값을 바꾼 후 아래 코드를 노트북에 붙여넣고 실행하세요.

`<api-key>`	Voyage AI API 키(6번 줄).
`<connection-string>`	MongoDB 클러스터 연결 문자열(18번째 줄)

다음 코드는 Voyage AI의 독점 임베딩 모델을 사용하여 벡터 임베딩을 생성하는 함수를 정의합니다. 구체적으로 이 코드는 다음을 수행합니다.

voyage-3-large 임베딩 모델을 지정합니다.
주어진 텍스트 입력에 대해 2048 차원 임베딩을 생성하기 위해 모델의 API를 호출하는 get_embedding()이라는 함수를 생성합니다.
Atlas 클러스터에 연결하여 3500개의 문서를 sample_mflix.embedded_movies 네임스페이스에서 가져옵니다.
get_embedding() 함수를 사용하여 각 문서의 title 필드에서 임베딩을 생성합니다.
MongoDB PyMongo 드라이버를 사용해 임베딩 값을 포함하는 title_voyageai_embedding라는 새 필드로 각 문서를 업데이트합니다.

1 import os
2 import pymongo
3 import voyageai
4 
5 # Specify your Voyage API key and embedding model
6 os.environ["VOYAGE_API_KEY"] = "<api-key>"
7 
8 model = "voyage-3-large"
9 outputDimension = 2048
10 vo = voyageai.Client()
11 # Define a function to generate embeddings
12 def get_embedding(data, input_type = "document"):
13   embeddings = vo.embed(
14       data, model = model, output_dimension = outputDimension, input_type = input_type
15   ).embeddings
16   return embeddings[0]
17 
18 # Connect to your MongoDB Atlas cluster
19 mongo_client = pymongo.MongoClient("<connection-string>")
20 db = mongo_client["sample_mflix"]
21 collection = db["embedded_movies"]
22 
23 # Filter to exclude null or empty plot fields
24 filter = {"title": {"$nin": [None, ""]}}
25 
26 # Get a subset of documents in the collection
27 documents = collection.find(filter).limit(3500)
28 
29 # Update each document with a new embedding field
30 updated_doc_count = 0
31 for doc in documents:
32     embedding = get_embedding(doc["title"])
33     if embedding is not None:
34         collection.update_one({"_id": doc["_id"]}, {"$set": {"title_voyageai_embedding": embedding}})
35         updated_doc_count += 1
36 
37 print(f"Updated {updated_doc_count} documents.")

Updated 3500 documents.

참고

이 작업이 완료되기까지 최대 15분이 걸릴 수 있습니다.

MongoDB Vector Search 인덱스 만들기

In this section, you create an MongoDB Vector Search index on the sample_mflix.embedded_movies namespace. The section demonstrates an index definition that can be used for running various queries. Before creating the index, complete the procedure to generate the embeddings.

`mongosh`를 사용하여 Atlas 클러스터에 연결합니다.

터미널 창에서 mongosh를 열고 Atlas 클러스터에 연결합니다. 연결에 대한 자세한 지침은 mongosh를 통해 연결을 참조하세요.

인덱스를 생성하려는 컬렉션이 포함된 데이터베이스로 전환합니다.

예시

use sample_mflix

switched to db sample_mflix

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

1 db.embedded_movies.createSearchIndex(
2   "multiple-vector-search", 
3   "vectorSearch", 
4   {
5     "fields": [
6       {
7         "type": "vector",
8         "path": "plot_embedding",
9         "numDimensions": 1536,
10         "similarity": "dotProduct"
11       },
12       {
13         "type": "vector",
14         "path": "plot_embedding_voyage_3_large",
15         "numDimensions": 2048,
16         "similarity": "dotProduct"
17       },
18       {
19         "type": "vector",
20         "path": "title_voyageai_embedding",
21         "numDimensions": 2048,
22         "similarity": "dotProduct"
23       }
24     ]
25   }
26 );

이 인덱스 정의는 다음 필드를 인덱싱합니다:

plot_embedding 필드를 vector 유형으로 지정합니다. 이 필드에는 영화 줄거리의 요약을 나타내는 벡터 임베딩을 포함합니다. 인덱스 정의는 다음과 같습니다.
- 1536개의 벡터 차원을 지정합니다.
- dotProduct 유사성을 사용하여 유사도를 측정합니다.
이 필드 매핑은 세 번째 사용 사례에 필요합니다.
plot_embedding_voyage_3_large 필드를 vector 유형으로 지정합니다. 이 필드에는 영화 줄거리의 요약을 나타내는 벡터 임베딩을 포함합니다. 인덱스 정의는 다음과 같습니다.
- 2048개의 벡터 차원을 지정합니다.
- dotProduct 유사성을 사용하여 유사도를 측정합니다.
이 필드 매핑은 이 튜토리얼의 모든 쿼리에 필요합니다.
title_voyageai_embedding 필드를 vector 유형으로 지정합니다. 이 필드에는 영화 제목을 나타내는 벡터 임베딩이 포함되어 있습니다. 인덱스 정의는 다음과 같습니다.
- 2048개의 벡터 차원을 지정합니다.
- dotProduct 유사성을 사용하여 유사도를 측정합니다.
이 필드 매핑은 두 번째 사용 사례에 필요합니다.

Run the MongoDB Vector Search Queries

이 섹션에서는 컬렉션에 생성한 multiple-vector-search라는 인덱스를 사용하여 sample_mflix 데이터베이스의 embedded_movies 컬렉션을 쿼리합니다.

쿼리에 사용할 임베딩을 위한 파일을 생성합니다.

embeddings.js이라는 파일을 만듭니다.
```
touch embeddings.js
```

다음 임베딩을 복사하여 embeddings.js 파일에 붙여넣습니다.

이 파일에는 쿼리에 사용된 쿼리 텀에 대한 임베딩이 포함되어 있습니다. 다음 표는 쿼리 텀, 해당 쿼리 텀의 임베딩을 포함하는 변수, 임베딩을 생성하는 데 사용된 임베딩 모델 그리고 차원 수를 보여줍니다.

쿼리 문구	변수	임베딩 모델	차원
light-hearted comedy with ghosts	COMEDY_INVOLVING_GHOSTS	Voyage AI's `voyage-3-large`	2048
slapstick humor with paranormal events	HUMOR_INVOLVING_PARANORMAL	Voyage AI's `voyage-3-large`	2048
battle between good and evil	BATTLE_GOOD_EVIL	Voyage AI's `voyage-3-large`	2048
journey across lands	JOURNEY_ACROSS_LANDS_VOYAGEAI	Voyage AI's `voyage-3-large`	2048
journey across lands	JOURNEY_ACROSS_LANDS_OPENAI	OpenAI의 `text-embedding-ada-002`	1536

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파일을 저장하고 닫습니다.

`mongosh`에서 클러스터에 연결합니다.

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

`sample_mflix` 데이터베이스를 사용합니다.

mongosh 프롬프트에서 다음 명령을 실행합니다.

use sample_mflix

쿼리 에 사용할 임베딩을 로드합니다.

아래 명령어를 실행하여 <path-to-file>을 본인의 embeddings.js 파일의 절대 경로로 바꾼 뒤 embeddings.js 파일에 임베딩을 로드합니다.
load('/<path-to-file>/embeddings.js';
true
임베딩이 성공적으로 로드됐는지 확인하세요.
아래와 비슷한 명령어를 실행하여 확인할 수 있습니다.
COMEDY_INVOLVING_GHOSTS.length
2048

Run the MongoDB Vector Search queries against the `embedded_movies` collection.

단일 문서 벡터에 대해 여러 쿼리 벡터 사용

의미적으로 유사한 텀을 찾기 위해 데이터셋을 포괄적으로 검색하여 어떤 쿼리 텀이 가장 좋은 결과를 반환하는지 확인합니다.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding_voyage_3_large",
11                 "queryVector": COMEDY_INVOLVING_GHOSTS,
12                 "numCandidates": 2000,
13                 "limit": 50
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": HUMOR_INVOLVING_PARANORMAL,
23                 "numCandidates": 2000,
24                 "limit": 50
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

[
  {
    _id: ObjectId('573a139af29313caabcef0a4'),
    plot: 'A paranormal expert and his daughter bunk in an abandoned house populated by 3 mischievous ghosts and one friendly one.',
    title: 'Casper',
    scoreDetails: {
      value: 0.01639344262295082,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 1,
          weight: 0.5,
          value: 0.8094863891601562,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.7794423699378967,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceaf10'),
    plot: "When Peter Plunkett's Irish castle turned hotel is about to be repossesed, he decides to spice up the attraction a bit for the 'Yanks' by having his staff pretend to haunt the castle. The ...",
    title: 'High Spirits',
    scoreDetails: {
      value: 0.01575682382133995,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 2,
          weight: 0.5,
          value: 0.7949001789093018,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 5,
          weight: 0.5,
          value: 0.7545012831687927,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce912c'),
    plot: 'Three unemployed parapsychology professors set up shop as a unique ghost removal service.',
    title: 'Ghostbusters',
    scoreDetails: {
      value: 0.015038444142921756,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 6,
          weight: 0.5,
          value: 0.7781286239624023,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 7,
          weight: 0.5,
          value: 0.7519128918647766,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a0f29313caabd056df'),
    plot: "A girl calls on her brother's imaginary friend to banish a mischievous boogeyman who has framed her for his pranks.",
    title: "Don't Look Under the Bed",
    scoreDetails: {
      value: 0.014913831197525408,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 13,
          weight: 0.5,
          value: 0.7534659504890442,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 2,
          weight: 0.5,
          value: 0.7626002430915833,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1399f29313caabced85b'),
    plot: 'A comical Gothic horror-movie-type family tries to rescue their beloved uncle from his gold-digging new love.',
    title: 'Addams Family Values',
    scoreDetails: {
      value: 0.014854753521126762,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.7640036344528198,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.7570045590400696,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13bdf29313caabd58456'),
    plot: 'Bertram Pincus is a man whose people skills leave much to be desired. When Pincus dies unexpectedly, but is miraculously revived after seven minutes, he wakes up to discover that he now has the annoying ability to see ghosts.',
    title: 'Ghost Town',
    scoreDetails: {
      value: 0.014709063378758382,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 7,
          weight: 0.5,
          value: 0.7774010300636292,
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    title: 'Loose in London',
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]

이 샘플 쿼리는 다음 입력 파이프라인 단계와 함께 $rankFusion을 사용합니다.

$vectorSearch

COMEDY_INVOLVING_GHOSTS 변수를 사용하여 쿼리의 queryVector 필드에 벡터 임베딩으로 지정된 light-hearted comedy with ghosts라는 문구를 plot_embedding_voyage_3_large 필드에서 검색합니다.
최대 2000개의 최근접 이웃을 검색 대상으로 지정합니다.
이 단계의 결과를 50개 문서로 제한합니다.
해당 파이프라인의 순위가 최종 점수에 미치는 영향으로 가중치 0.5를 지정합니다.

$vectorSearch

HUMOR_INVOLVING_PARANORMAL 변수를 사용하여 쿼리의 queryVector 필드에 벡터 임베딩으로 지정된 slapstick humor with paranormal events라는 문구를 plot_embedding_voyage_3_large 필드에서 순차적으로 벡터 검색을 수행합니다.
최대 2000개의 최근접 이웃을 검색 대상으로 지정합니다.
이 단계의 결과를 50개 문서로 제한합니다.
해당 파이프라인의 순위가 최종 점수에 미치는 영향으로 가중치 0.5를 지정합니다.

샘플 쿼리는 다음 파이프라인 단계를 지정합니다.

`$project`	결과에 `plot` 및 `title` 필드만 포함합니다. 결과에 `scoreDetails`라는 필드를 추가합니다.
`$limit`	반환되는 결과를 20개의 문서로 제한합니다.

MongoDB Vector Search merges the results for both the queries into a single result set. In the results:

scoreDetails.value는 해당 파이프라인의 원시 점수를 표시하며, 이는 가중치 적용과 상호 순위 융합 전의 값입니다.
score.details.rank는 해당 파이프라인 결과에서 문서의 순위를 표시합니다.
scoreDetails.details.value에는 가중치가 적용된 상호 순위 점수가 포함되어 있습니다.

다음을 수행할 수 있습니다.

쿼리 내 각 파이프라인에 할당된 가중치를 조정하여 결과를 더 세밀하게 조정할 수 있습니다.
분리된 결과가 표시되는 경우 결과에 포함된 문서 수를 늘리세요.

동일한 텀 쿼리 벡터를 여러 문서 벡터에 사용

데이터 세트의 여러 필드를 검색하여 동일한 쿼리에 대해 최상의 결과를 반환하는 필드를 확인합니다.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding_voyage_3_large",
11                 "queryVector": BATTLE_GOOD_EVIL,
12                 "numCandidates": 2000,
13                 "limit": 200
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "title_voyageai_embedding",
22                 "queryVector": BATTLE_GOOD_EVIL,
23                 "numCandidates": 2000,
24                 "limit": 200
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

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    scoreDetails: {
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      details: [
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    title: 'Demon Empire',
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    plot: 'A raging god of battle and a master samurai duke it out in a series of sword fights in a remote temple.',
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  {
    _id: ObjectId('573a13b6f29313caabd467f5'),
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    title: 'Battle of the Warriors',
    scoreDetails: {
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    _id: ObjectId('573a1398f29313caabceb2ff'),
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    _id: ObjectId('573a13c2f29313caabd67b22'),
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    title: 'Knights of Bloodsteel',
    scoreDetails: {
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    _id: ObjectId('573a1398f29313caabcebfc6'),
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    title: 'Heaven and Earth',
    scoreDetails: {
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    _id: ObjectId('573a139ef29313caabcfbd25'),
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    title: 'Hellboy',
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    title: 'Forest Warrior',
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    title: 'Storm Warriors',
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    title: 'Warriors of Heaven and Earth',
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이 샘플 쿼리는 다음 입력 파이프라인 단계와 함께 $rankFusion을 사용합니다.

$vectorSearch

BATTLE_GOOD_EVIL 변수를 사용하여 쿼리의 queryVector 필드에 벡터 임베딩으로 지정된 battle between good and evil이라는 문구를 plot_embedding_voyage_3_large 필드에서 검색합니다.
최대 2000개의 최근접 이웃을 검색 대상으로 지정합니다.
결과를 200개 문서로만 제한합니다.
해당 파이프라인의 순위가 최종 점수에 미치는 영향으로 가중치 0.5를 지정합니다.

$vectorSearch

BATTLE_GOOD_EVIL 변수를 사용하여 queryVector 필드에 벡터 임베딩으로 지정된 battle between good and evil이라는 string을 title_voyageai_embedding 필드에서 순차적으로 벡터 검색을 수행합니다.
최대 2000개의 최근접 이웃을 검색 대상으로 지정합니다.
결과를 200개 문서로만 제한합니다.
해당 파이프라인의 순위가 최종 점수에 미치는 영향으로 가중치 0.5를 지정합니다.

샘플 쿼리는 다음 파이프라인 단계를 지정합니다.

`$project`	결과에 `plot` 및 `title` 필드만 포함합니다. 결과에 `scoreDetails`라는 필드를 추가합니다.
`$limit`	반환되는 결과를 20개의 문서로 제한합니다.

MongoDB Vector Search merges the results for both the queries into a single result set. In the results:

scoreDetails.value는 해당 파이프라인의 원시 점수를 표시하며, 이는 가중치 적용과 상호 순위 융합 전의 값입니다.
score.details.rank는 해당 파이프라인 결과에서 문서의 순위를 표시합니다.
scoreDetails.details.value에는 가중치가 적용된 상호 순위 점수가 포함되어 있으며, 이 점수는 쿼리 텀에 대해 가장 관련성이 높은 결과를 반환하는 필드를 보여줍니다.

예를 들어, 결과의 첫 번째 및 네 번째 문서는 plot 필드에서 텀과의 유의미한 일치를 제안하고, 두 번째 및 다섯 번째 문서는 title 필드에서 유의미한 일치를 제안합니다. 다음을 수행할 수 있습니다.

쿼리 내 각 파이프라인에 할당된 가중치를 조정하여 결과를 더 세밀하게 조정할 수 있습니다.
분리된 결과가 표시되는 경우 결과에 포함된 문서 수를 늘리세요.

여러 모델을 사용한 동일 텀 쿼리

서로 다른 임베딩 모델의 임베딩을 검색하여 각 모델 간 의미 해석의 차이를 파악합니다.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding",
11                 "queryVector": JOURNEY_ACROSS_LANDS_OPENAI,
12                 "numCandidates": 2000,
13                 "limit": 100
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": JOURNEY_ACROSS_LANDS_VOYAGEAI,
23                 "numCandidates": 2000,
24                 "limit": 100
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

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    scoreDetails: {
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      details: [
        {
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          rank: 8,
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        {
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  {
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    title: 'Crumbs',
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  {
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    title: 'Foreign Land',
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  {
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    title: 'The Young and Prodigious T.S. Spivet',
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    title: 'She',
    scoreDetails: {
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  {
    _id: ObjectId('573a139af29313caabcf0e95'),
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    title: 'The 13th Warrior',
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    title: 'The Saragossa Manuscript',
    scoreDetails: {
      value: 0.012824675324675325,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 28,
          weight: 0.5,
          value: 0.9088537693023682,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 10,
          weight: 0.5,
          value: 0.7230479121208191,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13aef29313caabd2c923'),
    plot: 'A Chinese emissary is sent to the Gobi desert to execute a renegade soldier. When a caravan transporting a Buddhist monk and a valuable treasure is threatened by thieves, however, the two warriors might unite to protect the travelers.',
    title: 'Warriors of Heaven and Earth',
    scoreDetails: {
      value: 0.012658227848101266,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 19,
          weight: 0.5,
          value: 0.9103184342384338,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 19,
          weight: 0.5,
          value: 0.7152039408683777,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf02db'),
    plot: 'Chris embarks on an odyssey of self-discovery that spans the globe. Kidnapped and enslaved by gun smugglers, sold by pirates and thrust into the murky underworld of gambling and kickboxing,...',
    title: 'The Quest',
    scoreDetails: {
      value: 0.012507817385866166,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 18,
          weight: 0.5,
          value: 0.910321056842804,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 22,
          weight: 0.5,
          value: 0.7129499912261963,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13d6f29313caabd9eb15'),
    plot: 'A tribe of Norse warriors traipse across a barren land after battle. Bloodied and wounded, their chief is near death. He is about to hand over power to his son when an army of a completely different kind descends upon them.',
    title: 'Tumult',
    scoreDetails: {
      value: 0.012237762237762238,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 5,
          weight: 0.5,
          value: 0.9174321293830872,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 50,
          weight: 0.5,
          value: 0.7051275968551636,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b6f29313caabd46ae0'),
    plot: "In a countryside town bordering on a magical land, a young man makes a promise to his beloved that he'll retrieve a fallen star by venturing into the magical realm.",
    title: 'Stardust',
    scoreDetails: {
      value: 0.011742820602255552,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 1,
          weight: 0.5,
          value: 0.9228402376174927,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 81,
          weight: 0.5,
          value: 0.6994539499282837,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce8828'),
    plot: 'A warrior seeks his true origins in a seemingly prehistoric wasteland.',
    title: 'Yor, the Hunter from the Future',
    scoreDetails: {
      value: 0.01117448492608544,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.9153545498847961,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 61,
          weight: 0.5,
          value: 0.7023824453353882,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13aaf29313caabd218b4'),
    plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.',
    title: 'Coraline',
    scoreDetails: {
      value: 0.010934744268077601,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 21,
          weight: 0.5,
          value: 0.9102445840835571,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 45,
          weight: 0.5,
          value: 0.7056041955947876,
          details: []
        }
      ]
    }
  }
]

이 샘플 쿼리는 다음 입력 파이프라인 단계와 함께 $rankFusion을 사용합니다.

$vectorSearch

JOURNEY_ACROSS_LANDS_OPENAI 변수를 사용하여 쿼리의 queryVector 필드에 벡터 임베딩으로 지정된 journey across lands를 plot_embedding 필드에서 검색합니다.
최대 2000개의 최근접 이웃을 검색 대상으로 지정합니다.
결과를 100 문서로 제한합니다.
해당 파이프라인의 순위가 최종 점수에 미치는 영향으로 가중치 0.5를 지정합니다.

$vectorSearch

JOURNEY_ACROSS_LANDS_VOYAGEAI 변수를 사용하여 쿼리의 queryVector 필드에 벡터 임베딩으로 지정된 journey across lands를 plot_embedding_voyage_3_large 필드에서 순차적으로 벡터 검색합니다.
최대 2000개의 최근접 이웃을 검색 대상으로 지정합니다.
결과를 100 문서로 제한합니다.
해당 파이프라인의 순위가 최종 점수에 미치는 영향으로 가중치 0.5를 지정합니다.

샘플 쿼리는 다음 파이프라인 단계를 지정합니다.

`$project`	결과에 `plot` 및 `title` 필드만 포함합니다. 결과에 `scoreDetails`라는 필드를 추가합니다.
`$limit`	반환되는 결과를 20개의 문서로 제한합니다.

MongoDB Vector Search merges the results for both the queries into a single result set. In the results:

scoreDetails.value는 해당 파이프라인의 원시 점수를 표시하며, 이는 가중치 적용과 상호 순위 융합 전의 값입니다.
score.details.rank는 해당 파이프라인 결과에서 문서의 순위를 표시합니다.
scoreDetails.details.value에는 가중치가 적용된 상호 순위 점수가 포함되어 있으며, 이는 다양한 임베딩 모델에 의한 쿼리 텀의 의미 해석 강도와 차이를 보여줍니다.

예를 들어, 결과의 첫 번째와 다섯 번째 문서는 vectorPipeline2에서 사용된 모델이 더 유사한 의미 표현을 제안하는 반면, 결과의 두 번째와 네 번째 문서는 vectorPipeline1에서 사용된 모델이 더 가까운 의미 해석을 제안합니다.

돌아가기

벡터 검색과 전문 검색의 결합

호환되는 뷰 사용

1	import os
2	import pymongo
3	import voyageai
4
5	# Specify your Voyage API key and embedding model
6	os.environ["VOYAGE_API_KEY"] = "<api-key>"
7
8	model = "voyage-3-large"
9	outputDimension = 2048
10	vo = voyageai.Client()
11	# Define a function to generate embeddings
12	def get_embedding(data, input_type = "document"):
13	embeddings = vo.embed(
14	data, model = model, output_dimension = outputDimension, input_type = input_type
15	).embeddings
16	return embeddings[0]
17
18	# Connect to your MongoDB Atlas cluster
19	mongo_client = pymongo.MongoClient("<connection-string>")
20	db = mongo_client["sample_mflix"]
21	collection = db["embedded_movies"]
22
23	# Filter to exclude null or empty plot fields
24	filter = {"title": {"$nin": [None, ""]}}
25
26	# Get a subset of documents in the collection
27	documents = collection.find(filter).limit(3500)
28
29	# Update each document with a new embedding field
30	updated_doc_count = 0
31	for doc in documents:
32	embedding = get_embedding(doc["title"])
33	if embedding is not None:
34	collection.update_one({"_id": doc["_id"]}, {"$set": {"title_voyageai_embedding": embedding}})
35	updated_doc_count += 1
36
37	print(f"Updated {updated_doc_count} documents.")

1	db.embedded_movies.createSearchIndex(
2	"multiple-vector-search",
3	"vectorSearch",
4	{
5	"fields": [
6	{
7	"type": "vector",
8	"path": "plot_embedding",
9	"numDimensions": 1536,
10	"similarity": "dotProduct"
11	},
12	{
13	"type": "vector",
14	"path": "plot_embedding_voyage_3_large",
15	"numDimensions": 2048,
16	"similarity": "dotProduct"
17	},
18	{
19	"type": "vector",
20	"path": "title_voyageai_embedding",
21	"numDimensions": 2048,
22	"similarity": "dotProduct"
23	}
24	]
25	}
26	);

1	db.embedded_movies.aggregate([
2	{
3	$rankFusion: {
4	input: {
5	pipelines: {
6	vectorPipeline1: [
7	{
8	"$vectorSearch": {
9	"index": "multiple-vector-search",
10	"path": "plot_embedding_voyage_3_large",
11	"queryVector": COMEDY_INVOLVING_GHOSTS,
12	"numCandidates": 2000,
13	"limit": 50
14	}
15	}
16	],
17	vectorPipeline2: [
18	{
19	"$vectorSearch": {
20	"index": "multiple-vector-search",
21	"path": "plot_embedding_voyage_3_large",
22	"queryVector": HUMOR_INVOLVING_PARANORMAL,
23	"numCandidates": 2000,
24	"limit": 50
25	}
26	}
27	]
28	}
29	},
30	combination: {
31	weights: {
32	vectorPipeline1: 0.5,
33	vectorPipeline2: 0.5
34	}
35	},
36	"scoreDetails": true
37	}
38	},
39	{
40	"$project": {
41	_id: 1,
42	title: 1,
43	plot: 1,
44	scoreDetails: {"$meta": "scoreDetails"}
45	}
46	},
47	{
48	"$limit": 20
49	}
50	]);

사용 사례 및 이점

튜토리얼 소개

참고

임베딩 생성

전제 조건

절차

환경을 설정합니다.

벡터 임베딩을 생성하는 함수를 정의하고 사용합니다.

참고

MongoDB Vector Search 인덱스 만들기

mongosh를 사용하여 Atlas 클러스터에 연결합니다.

인덱스를 생성하려는 컬렉션이 포함된 데이터베이스로 전환합니다.

예시

db.collection.createSearchIndex() 메서드를 실행합니다.

Run the MongoDB Vector Search Queries

쿼리에 사용할 임베딩을 위한 파일을 생성합니다.

mongosh에서 클러스터에 연결합니다.

sample_mflix 데이터베이스를 사용합니다.

쿼리 에 사용할 임베딩을 로드합니다.

Run the MongoDB Vector Search queries against the embedded_movies collection.

단일 문서 벡터에 대해 여러 쿼리 벡터 사용

동일한 텀 쿼리 벡터를 여러 문서 벡터에 사용

여러 모델을 사용한 동일 텀 쿼리

`mongosh`를 사용하여 Atlas 클러스터에 연결합니다.

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

`mongosh`에서 클러스터에 연결합니다.

`sample_mflix` 데이터베이스를 사용합니다.

Run the MongoDB Vector Search queries against the `embedded_movies` collection.