mongodb/mongodb-atlas-local Docker イメージを使用して、MongoDB Search と MongoDB ベクトル検索を含む単一ノードレプリカセットを作成できます。mongodb/mongodb-atlas-local Docker イメージは、mongot を含む単一の MongoDB 配置であり、ローカル開発とテストのみを目的としています。単一ノードレプリカセット配置はポート 27017 で実行され、複数の mongot プロセスはサポートされません。この配置は本番環境での使用には適していません。
このチュートリアルでは、Docker Compose を使用して単一ノードレプリカセットで mongot を配置する方法を説明します。
前提条件
始める前に、以下のものをインストールしておく必要があります。
Docker
Docker Compose
mongoshローカルまたは Docker 内で
Docker Compose を使用した MongoDB Search と MongoDB ベクトル検索の配置
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最新の mongodb/mongodb-atlas-local Docker イメージをプルします。
mongodb/mongodb-atlas-local Dockerイメージをプルするには、次のコマンドを実行します。
docker pull mongodb/mongodb-atlas-local:preview
注意
mongot の最新バージョンを取得するには、preview タグを使用する必要があります。previewタグは、自動埋め込みをサポートするmongotのCommunityビルドをバンドルします。previewタグのない画像には、mongotのCommunityビルドは含まれず、自動埋め込みもサポートされません。latestタグは、latestに固定された既存のツールまたはスクリプトがあり、自動埋め込みが不要な場合にのみ使用します。
再現可能な環境を作成するには、preview や latest のような可変タグではなく、特定のバージョン タグにイメージを固定します。<version> を使用して、必要なバージョン タグに置き換えます。
docker pull mongodb/mongodb-atlas-local:<version>
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docker-compose.yaml ファイルを作成します。
docker-compose.yamlファイルを作成します。完全で詳細な例については、Docker Compose 例を参照してください。
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:
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プレースホルダー値を置き換えてファイルを保存します。
<username> と <password> のプレースホルダー値を自分の値に置き換えて、ファイルを保存します。ファイルを使用してユーザー名とパスワードを設定して、セキュリティを強化することもできます。MONGODB_INITDB_ROOT_USERNAME_FILE と MONGODB_INITDB_ROOT_PASSWORD_FILE の環境変数を使用して、ユーザー名とパスワードが含まれるファイルへのパスを指定します。
自動埋め込みを使用するには、環境変数 VOYAGE_API_KEY を介して、Voyage AI API キー（Atlas UI から作成）を提供します。デフォルトでは、埋め込みプロバイダーの**エンドポイントとなる接続されたデバイス**は https://ai.mongodb.com/v1/embeddings （Atlas UI から作成されたキーの場合）です。
注意
Voyage AI から直接 API キーを作成した場合は、環境変数 VOYAGE_EMBEDDING_PROVIDER_ENDPOINT を使用して埋め込みプロバイダーのエンドポイントも提供する必要があります。この変数の値は https://api.voyageai.com/v1/embeddings です。
詳細については、MongoDB ベクトル検索自動埋め込みのmongotを構成するを参照してください。
初期化中にMongoDBサンプルデータセットを自動的にロードするには、MONGODB_LOAD_SAMPLE_DATA 環境変数を true に設定します。
自動埋め込みとサンプルデータの docker-compose.yaml 設定
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 と MongoDB ベクトル検索を接続して使用する mongosh
1
2
(任意).サンプル データセットをロードした場合は、インデックスを作成します。
同じデータがロードされていることを確認するには、次のコマンドを実行します。
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 インデックスを作成するには、
sample_mflixデータベースに切り替えます。use sample_mflix sample_mflix.moviesコレクションにmovie-title-indexという名前の MongoDB Search インデックスを作成するには、次のコマンドを実行します。次のインデックス定義により、
moviesコレクションにmovie-indexという名前の MongoDB Search インデックスが作成されます。インデックス定義では、コレクションのドキュメント内のすべての動的にインデックス可能なフィールドを自動的にインデックス化するため、mappingsがdynamic: trueと指定されます。1 db.movies.createSearchIndex( 2 "movie-index", 3 { mappings: { dynamic: true } } 4 ) sample_mflix.embedded_moviesコレクションにmovie-vector-indexという名前の MongoDB ベクトル検索インデックスを作成するには、次のコマンドを実行します。次のインデックス定義により、
embedded_moviesコレクションのplot_embedding_voyage_4_largeフィールドにmovie-vector-indexという名前の MongoDB ベクトル検索インデックスが作成されます。インデックス定義では、以下を指定します。MongoDB ベクトル検索インデックスを作成するために、
typeをvectorSearchとして使用します。フィールド内のベクトル埋め込みにインデックスを付けるための
plot_embedding_voyage_4_largeとしてのpath。numDimensionsとして2048を使用してベクトル埋め込みの次元数を指定します。similarityをdotProductとして、クエリベクトルとインデックスされたベクトル間の類似度を測定するために使用する類似度関数を指定します。
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 ) インデックスが作成されたことを確認するには、次のコマンドを実行します。
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
(任意).インデックスを作成した場合は、インデックスでクエリを実行します。
movie-indexインデックスで MongoDB Search クエリを実行するには、次のコマンドを実行します。このクエリでは、
movie-indexインデックスを使用して、moviesコレクションのplotフィールドで期間courtroom lawyerを検索します。クエリでは、fuzzyオプションを使用してファジーマッチングを有効にします。クエリは、上位 3 件の結果を返し、titleフィールドとplotフィールドのみをプロジェクトします。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-indexインデックスで MongoDB ベクトル検索クエリを実行するには、次の手順を実行します。query-embeddings.jsという名前のファイルを作成します。touch query-embeddings.js 詞句
courtroom lawyerの次の埋め込みをコピーしてquery-embeddings.jsファイルに貼り付けます。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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次のクエリでは、
$vectorSearchステージを使用して次の処理が行われます。string
courtroom lawyerのベクトル埋め込みを使用して、plot_embedding_voyage_4_largeフィールドで近似最近傍探索を行います。最大
100の最近傍を考慮しますが、出力は 3 の結果のみに制限されます。
1 db.embedded_movies.aggregate([ 2 { 3 "$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 } ]
詳細
Atlas Local を配置するには、次を使用します。
Docker (
run)、詳細は「ローカルAtlas配置の作成」を参照してください。Atlas CLI (
setup)の詳細については、「ローカル Atlas 配置の作成」を参照してください。MongoDB MCP サーバーについては、MongoDB MCP サーバーの使用を開始するを参照してください。
MongoDB Community Edition で mongot を配置するには、次の手順で行います。
Red Hat、Ubuntu、Debian、SUSE、Amazon Linux などの Linux ディストリビューションについては、「Tarball を使用した MongoDB Search と MongoDB ベクトル検索 の配置する」を参照してください。
Docker の詳細については、「Docker で MongoDB Search と MongoDB ベクトル検索 を配置する」を参照してください。
Kubernetes Operator 用 MongoDB Controllers については、「Kubernetes Operator 用 MongoDB Controllers を使用した MongoDB Search と MongoDB Vector Search の配置」を参照してください。
MongoDB Enterprise Edition で mongot を配置するには、Kubernetes 演算子 用 MongoDB コントローラーを使用した MongoDB Search と MongoDB ベクトル検索 の配置を参照してください。