mongodb/mongodb-atlas-local Docker 이미지를 사용하여 MongoDB Search와 MongoDB 벡터 검색이 포함된 단일 노드 복제본 세트를 만들 수 있습니다. mongodb/mongodb-atlas-local Docker 이미지는 mongot 를 포함하는 자극 완비된 MongoDB 배포서버이며 로컬 개발 및 테스트 용도로만 사용할 수 있습니다. 단일 노드 복제본 세트 배포서버는 포트 27017 에서 실행되며 여러 mongot 프로세스를 지원하지 않습니다. 이 배포서버는 프로덕션 사용에 적합하지 않습니다.
이 튜토리얼에서는 Docker Compose를 사용하여 단일 노드 복제본 세트로 mongot 를 배포하는 방법을 설명합니다.
전제 조건
시작하기 전에 다음이 설치되어 있어야 합니다.
Docker
Docker Compose
mongosh로컬이나 Docker에서
Docker Compose로 MongoDB Search 및 MongoDB 벡터 검색 배포
1
최신 mongodb/mongodb-atlas-local Docker 이미지를 가져옵니다.
mongodb/mongodb-atlas-local Docker 이미지를 가져오려면 다음 명령을 실행 .
docker pull mongodb/mongodb-atlas-local:preview
참고
mongot 의 최신 버전을 가져오려면 preview 태그를 사용해야 합니다. preview 태그는 자동 임베딩을 지원하는 mongot의 Community 빌드를 번들링합니다. preview 태그가 없는 이미지에는 mongot 의 Community 빌드가 포함되지 않거나 자동 임베딩을 지원하지 않습니다. latest 태그는 latest 에 고정된 기존 도구 또는 스크립트가 있고 자동 임베딩이 필요하지 않은 경우에만 사용합니다.
반복 가능한 환경을 만들려면 preview 또는 latest 같은 이동 태그 대신 이미지를 특정 버전 태그에 고정합니다. <version> 를 원하는 버전 태그로 대체합니다.
docker pull mongodb/mongodb-atlas-local:<version>
2
docker-compose.yaml 파일 만듭니다.
docker-compose.yaml 파일을 만듭니다. 완전하고 자세한 예시를 보려면 Docker Compose 예시를 참조하세요.
1 services: 2 mongodb: 3 image: mongodb/mongodb-atlas-local:preview 4 hostname: mongodb 5 environment: 6 - MONGODB_INITDB_ROOT_USERNAME=<username> 7 - MONGODB_INITDB_ROOT_PASSWORD=<password> 8 ports: 9 - 27017:27017 10 volumes: 11 - ./init:/docker-entrypoint-initdb.d 12 - db:/data/db 13 - configdb:/data/configdb 14 - mongot:/data/mongot 15 volumes: 16 db: 17 configdb: 18 mongot:
3
자리 표시자 값을 바꾸고 파일을 저장합니다.
<username> 과 <password> 자리 표시자 값을 원하는 값으로 바꾸고 파일을 저장합니다. 보안 강화를 위해 파일을 통해 사용자 이름과 비밀번호를 설정할 수도 있습니다. MONGODB_INITDB_ROOT_USERNAME_FILE 과 MONGODB_INITDB_ROOT_PASSWORD_FILE 환경 변수를 사용하여 사용자 이름과 비밀번호가 포함된 파일의 경로를 지정합니다.
자동 임베딩을 사용하려면 환경 변수 VOYAGE_API_KEY를 통해 Voyage AI API 키(Atlas UI에서 만들었음) 를 제공합니다. 기본적으로 임베딩 제공자 엔드포인트는 https://ai.mongodb.com/v1/embeddings (Atlas UI에서 만들어진 키의 경우) 입니다.
참고
Voyage AI에서 API 키를 직접 생성한 경우 환경 변수 VOYAGE_EMBEDDING_PROVIDER_ENDPOINT를 통해 임베딩 제공자 엔드포인트도 제공해야 합니다. 이 변수의 값은 https://api.voyageai.com/v1/embeddings입니다.
자세히 학습하려면 자동 임베딩을 위한 MongoDB 벡터 검색을 위한 mongot 구성.을 참조하세요.
초기화 동안 MongoDB 샘플 데이터 세트를 자동으로 로드하려면 MONGODB_LOAD_SAMPLE_DATA 환경 변수를 true(으)로 설정합니다.
자동 임베딩 및 샘플 데이터에 대한 docker-compose.yaml 설정
1 services: 2 mongodb: 3 image: mongodb/mongodb-atlas-local:preview 4 hostname: mongodb 5 environment: 6 - MONGODB_INITDB_ROOT_USERNAME_FILE=<path-to-username-file> 7 - MONGODB_INITDB_ROOT_PASSWORD_FILE=<path-to-password-file> 8 - MONGODB_LOAD_SAMPLE_DATA=true 9 - VOYAGE_API_KEY=<voyage-api-key>
4
MongoDB Search 및 MongoDB 벡터 검색 연결 및 사용 mongosh
1
2
(선택 사항). 샘플 데이터 세트를 로드한 경우 인덱스를 생성합니다.
동일한 데이터가 로드되었는지 확인하려면 다음 명령을 실행합니다.
show dbs __mdb_internal_search 56.00 KiB admin 252.00 KiB config 172.00 KiB local 193.25 MiB sample_airbnb 52.52 MiB sample_analytics 9.48 MiB sample_geospatial 1.15 MiB sample_guides 40.00 KiB sample_mflix 96.16 MiB sample_restaurants 6.43 MiB sample_supplies 1.04 MiB sample_training 46.91 MiB sample_weatherdata 2.58 MiB 인덱스를 만들려면
sample_mflix데이터베이스로 전환하세요.use sample_mflix sample_mflix.movies컬렉션에movie-title-index라는 MongoDB Search 인덱스를 생성하려면 다음 명령을 실행하세요.다음 인덱스 정의는
movies컬렉션에movie-index라는 이름의 MongoDB Search 인덱스를 만들어줍니다. 인덱스 정의는 컬렉션 문서의 모든 동적 인덱싱 가능 필드를 자동으로 인덱싱하도록mappings를dynamic: true로 지정합니다.1 db.movies.createSearchIndex( 2 "movie-index", 3 { mappings: { dynamic: true } } 4 ) sample_mflix.embedded_movies컬렉션에movie-vector-index라는 이름의 MongoDB 벡터 검색 인덱스를 만들려면 다음 명령을 실행합니다.다음 인덱스 정의는
embedded_movies컬렉션의plot_embedding_voyage_4_large필드에movie-vector-index라는 이름의 MongoDB 벡터 검색 인덱스를 만듭니다. 인덱스 정의는 다음을 지정합니다.type을vectorSearch로 사용하여 MongoDB 벡터 검색 인덱스를 만듭니다.path을plot_embedding_voyage_4_large로 사용하여 필드의 벡터 임베딩에 인덱스를 만듭니다.numDimensions을2048로 사용하여 벡터 임베딩의 차원 수를 지정합니다.similarity을dotProduct(으)로 유사성 함수를 지정하여 쿼리 벡터와 인덱스 벡터 간의 유사성을 측정하는 데 사용합니다.
1 db.embedded_movies.createSearchIndex( 2 "movie-vector-index", 3 "vectorSearch", 4 { 5 fields: [ 6 { type: "vector", path: "plot_embedding_voyage_4_large", numDimensions: 2048, similarity: "dotProduct" } 7 ] 8 } 9 ) 인덱스가 생성되었는지 확인하려면, 다음 명령을 실행합니다.
db.movies.getSearchIndexes().forEach(printjson) [ { id: '6a22ddd3709bac3ce7a37252', name: 'movie-index', status: 'READY', queryable: true, latestDefinitionVersion: { version: 0, createdAt: ISODate('2026-06-05T14:31:47.000Z') }, latestDefinition: { mappings: { dynamic: true } }, statusDetail: [ { hostname: '6a22dbdcb2393a40012a14b2', status: 'READY', queryable: true, mainIndex: { status: 'READY', queryable: true, definitionVersion: { version: 0, createdAt: ISODate('2026-06-05T14:31:47.000Z') }, definition: { mappings: { dynamic: true, fields: {} } } } } ], numDocs: 21349 }, { id: '6a22dde9709bac3ce7a37254', name: 'movie-vector-index', status: 'READY', queryable: true, latestDefinitionVersion: { version: 0, createdAt: ISODate('2026-06-05T14:32:09.000Z') }, latestDefinition: { fields: [ { type: 'vector', path: 'plot_embedding_voyage_4_large', numDimensions: 2048, similarity: 'dotProduct' } ] }, statusDetail: [ { hostname: '6a22dbdcb2393a40012a14b2', status: 'READY', queryable: true, mainIndex: { status: 'READY', queryable: true, definitionVersion: { version: 0, createdAt: ISODate('2026-06-05T14:32:09.000Z') }, definition: { fields: [ { type: 'vector', path: 'plot_embedding_voyage_4_large', numDimensions: 2048, similarity: 'dotProduct' } ] } } } ], numDocs: 21349 } ]
3
(선택 사항). 인덱스를 생성한 경우 인덱스에 대해 쿼리를 실행합니다.
movie-index인덱스에서 MongoDB Search 쿼리를 실행하려면 다음 명령을 실행합니다.이 쿼리는
movie-index인덱스를 사용하여movies컬렉션의plot필드에서courtroom lawyer터버를 검색합니다. 쿼리는fuzzy옵션을 사용하여 자유 일치를 활성화합니다. 쿼리는 상위 3 결과를 반환하고title및plot필드만 프로젝트합니다.1 db.movies.aggregate([ 2 { 3 "$search": { 4 "index": "movie-index", 5 "text": { 6 "query": "courtroom lawyer", 7 "path": "plot", 8 "fuzzy": {} 9 } 10 } 11 }, 12 { 13 "$limit": 3 14 }, 15 { 16 "$project": { 17 "_id": 0, 18 "title": 1, 19 "plot": 1 20 } 21 } 22 ]) [ { plot: 'Academy Award-winning documentary filmmaker, Jean-Xavier de Lestrade, presents a gripping courtroom thriller, offering a rare and revealing inside look at a high-profile murder trial. In ...', title: 'Death on the Staircase' }, { plot: 'William Kunstler was one of the most famous lawyers of the 20th century. The New York Times called him "the most hated and most loved lawyer in America." His clients included Martin Luther ...', title: 'William Kunstler: Disturbing the Universe' }, { plot: "A dying millionnaire has her soul transferred into a younger, willing woman. But something goes wrong, and she finds herself in her lawyer's body - together with the lawyer.", title: 'All of Me' } ] movie-vector-index인덱스에서 MongoDB 벡터 검색 쿼리를 실행하려면 다음 단계를 수행합니다.query-embeddings.js이라는 파일을 만듭니다.touch query-embeddings.js 구
courtroom lawyer에 대한 다음 임베딩을 복사하여query-embeddings.js파일에 붙여넣습니다:QUERY_EMBEDDING=[0.021390099078416824, 0.0055892206728458405, -0.02259857952594757, -0.005287100560963154, -0.04519715905189514, -0.026828259229660034, 0.004954768810421228, 0.01788550615310669, -0.016193633899092674, 0.010574201121926308, 0.0007590766181237996, -0.03963815048336983, 0.00014633940008934587, 0.02054416388273239, -0.025015540421009064, -0.0024169604294002056, 0.033112358301877975, -0.007250881288200617, -0.01492473017424345, 0.009969961829483509, 0.0055892206728458405, -0.006858124863356352, 0.008217665366828442, -0.011359713971614838, 0.005256888922303915, -0.04906429350376129, -0.016435330733656883, 0.03142048418521881, -0.009486569091677666, 0.03915475681424141, -0.014078794047236443, -0.027311652898788452, 0.014078794047236443, -0.01208480168133974, 0.0006080165621824563, 0.028157588094472885, 0.047855816781520844, 0.016797874122858047, -0.03552931547164917, -0.00486413249745965, -0.005679856985807419, 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다음 쿼리는
$vectorSearch단계를 사용하여 다음을 수행합니다.string
courtroom lawyer에 대한 벡터 임베딩을 사용하여plot_embedding_voyage_4_large필드에서 근사 최근접귷리를 검색합니다.가장 가까운 이웃을 최대
100개까지 고려하지만 출력은 3 개의 결과로만 제한합니다.
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "movie-vector-index", 5 "path": "plot_embedding_voyage_4_large", 6 "queryVector": QUERY_EMBEDDING, 7 "numCandidates": 100, 8 "limit": 3 9 } 10 }, 11 { 12 "$project": { 13 "_id": 0, 14 "title": 1, 15 "plot": 1, 16 "score": { $meta: "vectorSearchScore" } 17 } 18 } 19 ]) [ { plot: 'An attorney defends an officer on trial for ordering his troops to fire on civilians after they stormed a U.S. embassy in a third world country.', title: 'Rules of Engagement', score: 0.695757269859314 }, { plot: 'An American attorney on business in China, ends up wrongfully on trial for murder and his only key to innocence is a female defense lawyer from the country.', title: 'Red Corner', score: 0.6952665448188782 }, { plot: 'A lawyer becomes a target by a corrupt politician and his NSA goons when he accidentally receives key evidence to a serious politically motivated crime.', title: 'Enemy of the State', score: 0.6868826150894165 } ]
자세히 알아보기
다음을 사용하여 Atlas Local을 배포합니다.
Docker(
run)에 대해서는 로컬 Atlas 배포를 만듭니다.를 참조하세요.Atlas CLI(
setup)에 대해서는 로컬 Atlas 배포 생성을 참조하십시오.MongoDB MCP 서버에 대해서는 MongoDB MCP 서버 시작하기를 참조하십시오.
MongoDB Community Edition과 함께 mongot을 배포하려면:
Red Hat, Ubuntu, Debian, SUSE 또는 Amazon Linux와 같은 Linux 배포판은 타볼로 MongoDB Search 및 MongoDB 벡터 검색 배포를 참조하세요.
Docker에 대해서는 Docker에 MongoDB Search와 MongoDB Vector Search 배포를 참조하십시오.
Kubernetes 연산자용 MongoDB Controllers에 대한 자세한 내용은 Kubernetes 연산자용 MongoDB Controllers를 사용하여 MongoDB Search 및 MongoDB 벡터 검색 배포를 참조하세요.
MongoDB Enterprise Edition으로 mongot를 배포하려면 Kubernetes 연산자용 MongoDB 컨트론러를 사용하여 MongoDB Search 및 MongoDB 벡터 검색 배포를 참조하십시오.