Kubernetes演算子を使用したMongoDB Search およびMongoDB ベクトル検索 の配置のサポートは、プレビュー機能として利用できます。機能および関連するドキュメントは、プレビュー期間中にいつでも変更される可能性があります。詳しくは、プレビュー機能を参照してください。
MongoDBSearchリソースを正常にインストールすると、 MongoDB Search とベクトル検索インデックスを mdb-user として作成し、インデックスを使用してMongoDB Search とベクトル検索クエリを実行できます。このページでは、サンプルデータのロード、データ用のMongoDB Search およびベクトル検索インデックスの作成、データに対してMongoDB Search およびベクトル検索クエリを実行する方法について説明します。
前提条件
MongoDB Search とベクトル検索を使用するには、次のものが必要です。
MongoDBとMongoDBSearchの正常な配置。詳しくは以下を参照してください。
MongoDB Search とベクトル検索を作成し、 MongoDB Search とベクトル検索 のクエリを実行する権限を持つユーザー。
詳細については、「 MongoDB Search およびベクトル検索用のCloud ManagerまたはMongoDB Ops Managerユーザーの構成 」を参照してください。
Kubernetesコマンドライン ツール（
kubectl）は、クラスターと通信するように構成されています。このチュートリアルのコマンドを実行中には、Batch v5.1 以上が必要です。
手順
1
環境変数を設定してください。
この手順の後続のステップで使用する環境変数を設定するには、以下をコピーし、環境変数の値を設定してから、環境変数をロードします。
1 # The env vars here are all commented out because this snippets module 2 # is reusable across different MongoDB deployments and all the necessary variables 3 # should be already defined there. 4 5 # set it to the context name of the k8s cluster 6 #export K8S_CTX="<local cluster context>" 7 # the following namespace will be used to deploy mongodb tools pod 8 #export MDB_NS="mongodb" 9 10 # regular user performing restore and search queries on sample mflix database 11 # the user should be able to restore database using mongorestore 12 #export MDB_USER_PASSWORD="mdb-user-password-CHANGE-ME" 13 14 # name of MongoDB or MongoDBCommunity resource in case it's deployed in the same cluster 15 # user only for the connection string in MDB_CONNECTION_STRING env var below 16 #export MDB_RESOURCE_NAME="mdbc-rs" 17 18 # default connection string if MongoDB database is deployed using the operator 19 #export MDB_CONNECTION_STRING="mongodb://mdb-user:${MDB_USER_PASSWORD}@${MDB_RESOURCE_NAME}-0.${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local:27017/?replicaSet=${MDB_RESOURCE_NAME}"
2
MongoDBツール ポッドを配置します。
mongodb-tools-pod という名前のポッドを配置して、 Kubernetesクラスター内から mongosh や mongorestore などのMongoDBクライアントツールを実行します。クラスター内でポッドを実行すると、データベースを外部に公開する必要なく、 MongoDBへの接続が簡素化されます。
次のコマンドは、 MongoDB Community Server イメージを実行する mongodb-tools-pod という名前のポッドを配置します。ポッドは sleep infinity コマンドで実行中され、kubectl exec でMongoDBクライアントツールを実行できるようになります。
1 kubectl apply -n "${MDB_NS}" --context "${K8S_CTX}" -f - <<EOF 2 apiVersion: v1 3 kind: Pod 4 metadata: 5 name: mongodb-tools-pod 6 labels: 7 app: mongodb-tools 8 spec: 9 containers: 10 - name: mongodb-tools 11 image: mongodb/mongodb-community-server:${MDB_VERSION}-ubi8 12 command: ["/bin/bash", "-c"] 13 args: ["sleep infinity"] 14 restartPolicy: Never 15 EOF 16 17 echo "Waiting for the mongodb-tools to be ready..." 18 kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait \ 19 --for=condition=Ready pod/mongodb-tools-pod --timeout=60s
3
サンプルデータをインポートします。
次のコレクションを含む sample_mflixデータベースをインポートします。
コレクション名 | 説明 |
|---|---|
| 特定の映画に関連付けられたコメントが含まれます。 |
| 次の追加フィールドを持つ、
|
| 公開年、監督、レビューなどの映画情報が含まれています。 |
| メタデータ フィールド。ユーザーの JSON Web トークンが含まれます。 |
| 映画館のロケーションが含まれています。 |
| ユーザー情報が含まれています。 |
次のコマンドはサンプルデータセットをダウンロードし、mongorestore を使用してMongoDBデプロイの sample_mflixデータベースにデータを読み込み、mdb-user として接続します。
1 kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" \ 2 mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF 3 echo "Downloading sample database archive..." 4 curl https://atlas-education.s3.amazonaws.com/sample_mflix.archive \ 5 -o /tmp/sample_mflix.archive 6 echo "Restoring sample database" 7 mongorestore \ 8 --archive=/tmp/sample_mflix.archive \ 9 --verbose=1 \ 10 --drop \ 11 --nsInclude 'sample_mflix.*' \ 12 --uri="${MDB_CONNECTION_STRING}" 13 EOF 14 )"
4
インデックスを作成します。
MongoDB Search またはベクトル検索インデックスを作成できます。$search および $searchMeta クエリを実行するにはMongoDB Searchインデックスを作成し、$vectorSearch クエリを実行するにはベクトル検索インデックスを作成する必要があります。
MongoDB Search は、動的および静的フィールドマッピング、さまざまなアナライザ（標準、言語固有、カスタム）、シノニム（同意語）やファセット検索などの機能をサポートする柔軟なインデックス定義を提供します。詳しくは、「 インデックスの定義 」を参照してください。
ベクトル検索 は、Ann および ENN 検索アルゴリズムを使用して、セマンティック類似性に基づいてデータへのベクトル埋め込みのクエリをサポートする簡単なインデックス定義を提供します。詳細については、 「ベクトル検索インデックスの定義」 を参照してください。
ターミナルで次のコマンドをコピー、貼り付け、実行してMongoDB Search またはベクトル検索インデックスを作成します。
コマンドは
kubectl execを使用してmongodb-tools-podでmongoshを実行します。sample_mflixdb.<collection>.createSearchIndex()メソッドに接続してインデックスを作成します。次のコマンドは、
moviesコレクションに動的マッピングを含む、defaultという名前のMongoDB Searchインデックスを作成します。動的マッピングにより、動的にインデックス付け可能なすべてのフィールドタイプが自動的にインデックス化されます。1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \ 2 mongosh --quiet \ 3 "${MDB_CONNECTION_STRING}" \ 4 --eval "use sample_mflix" \ 5 --eval 'db.movies.createSearchIndex("default", { mappings: { dynamic: true } });' 次のコマンドは、DN クエリを実行中ための コレクション内の 数の次元を持つ フィールドに
vector_indexplot_embedding_voyage_3_large2048embedded_moviesという名前のベクトル検索インデックスを作成します。1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \ 2 mongosh --quiet "${MDB_CONNECTION_STRING}" \ 3 --eval "use sample_mflix" \ 4 --eval 'db.embedded_movies.createSearchIndex("vector_index", "vectorSearch", 5 { "fields": [ { 6 "type": "vector", 7 "path": "plot_embedding_voyage_3_large", 8 "numDimensions": 2048, 9 "similarity": 10 "dotProduct", 11 "quantization": "scalar" 12 } ] });' インデックスの作成が完了するまで待ちます。
インデックス作成は非同期プロセスです。次のスクリプトを実行して、インデックスの作成が完了するまで 60 秒待ちます。
1 Currently it's not possible to check the status of search indexes, we need to just wait 2 echo "Sleeping to wait for search indexes to be created" 3 sleep 60 または、次のコマンドを実行して、コレクションのインデックスのリストを取得します。
1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \ 2 mongosh --quiet "${MDB_CONNECTION_STRING}" \ 3 --eval "use sample_mflix" \ 4 --eval 'db.runCommand({"listSearchIndexes": "movies"});' 1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \ 2 mongosh --quiet "${MDB_CONNECTION_STRING}" \ 3 --eval "use sample_mflix" \ 4 --eval 'db.runCommand({"listSearchIndexes": "embedded_movies"});'
5
インデックスを使用してデータをクエリします。
インデックスの作成が成功すると、インデックスを使用してMongoDB Search またはベクトル検索クエリを実行できます。MongoDB Search とベクトル検索を組み合わせることもできます。MongoDB Search クエリの実行中の詳細については、クエリの定義を参照してください。ベクトル検索クエリの詳細については、 ベクトル検索クエリの実行 を参照してください。
次のスクリプトは、集計パイプラインクエリ条件を定義し、kubectl exec コマンドを使用して mongosh をロード してクエリを実行します。
次のクエリは、plotフィールドで baseball というタームを含む映画のプロットを検索します。comedy ジャンルと romance ジャンルの映画は除外されます。
1 mdb_script=$(cat <<'EOF' 2 use sample_mflix; 3 db.movies.aggregate([ 4 { 5 $search: { 6 "compound": { 7 "must": [ { 8 "text": { 9 "query": "baseball", 10 "path": "plot" 11 } 12 }], 13 "mustNot": [ { 14 "text": { 15 "query": ["Comedy", "Romance"], 16 "path": "genres" 17 } 18 } ] 19 }, 20 "sort": { 21 "released": -1 22 } 23 } 24 }, 25 { 26 $limit: 3 27 }, 28 { 29 $project: { 30 "_id": 0, 31 "title": 1, 32 "plot": 1, 33 "genres": 1, 34 "released": 1 35 } 36 } 37 ]); 38 EOF 39 ) 40 41 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" \ 42 mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF 43 echo '${mdb_script}' > /tmp/mdb_script.js 44 mongosh --quiet "${MDB_CONNECTION_STRING}" < /tmp/mdb_script.js 45 EOF 46 )"
1 mdbc-rs [primary] test> switched to db sample_mflix 2 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [ 3 { 4 plot: 'A sports agent stages an unconventional recruitment strategy to get talented Indian cricket players to play Major League Baseball.', 5 genres: [ 'Biography', 'Drama', 'Sport' ], 6 title: 'Million Dollar Arm', 7 released: ISODate('2014-05-16T00:00:00.000Z') 8 }, 9 { 10 plot: 'A Taiwanese high school baseball team travels to Japan in 1931 to compete in a national tournament.', 11 genres: [ 'Biography', 'Drama', 'History' ], 12 title: 'Kano', 13 released: ISODate('2014-02-27T00:00:00.000Z') 14 }, 15 { 16 plot: "12-year-old Josh is a mixed race boy and a promising baseball player. He is abused by his mother's boyfriend Byrd, and neglected by his mother Debbie. He forges his own path in life when ...", 17 genres: [ 'Drama' ], 18 title: 'Calloused Hands', 19 released: ISODate('2013-03-03T00:00:00.000Z') 20 } 21 ] 22 mdbc-rs [primary] sample_mflix>
次のクエリは、投票 AI の voyage-3-large モデルを使用して生成された文字列 time travel を、2048 次元ベクトル埋め込みを使用して plot_embedding_voyage_3_largeフィールドで検索します。
1 mdb_script=$(cat <<'EOF' 2 use sample_mflix; 3 db.embedded_movies.aggregate([ 4 { 5 "$vectorSearch": { 6 "index": "vector_index", 7 "path": "plot_embedding_voyage_3_large", 8 "queryVector": [-0.034731735,0.008558298,-0.0153717,-0.029912498,0.011549547,0.010261648,-0.011964999,-0.023265276,0.010303194,-0.006896493,-0.00054528,0.003926015,-0.025757983,0.027419789,0.001199616,-0.036227357,-0.005297005,0.021935832,0.010303194,-0.019193852,0.025093261,-0.040049512,-0.033900831,-0.011466458,-0.01827986,-0.0153717,0.023265276,0.007727395,0.000114249,0.005317777,-0.043871664,-0.02127111,-0.019609304,0.016368784,-0.004756918,0.003552109,0.006522586,-0.005400868,-0.015620971,-0.034565553,-0.018695312,-0.023099095,0.050851244,-0.034731735,0.004819236,0.022268193,-0.095719993,0.05517194,-0.046198189,-0.036393538,0.007187308,-0.02459472,-0.036725901,0.009472291,0.019027673,0.020938748,-0.011051006,0.027087428,0.04586583,-0.022600554,-0.05517194,0.044204023,0.01213118,0.047859997,-0.03938479,0.002928932,0.002056484,0.019443123,-0.028583053,0.013543714,0.022932915,0.011632638,0.004923099,0.000389486,0.020024756,-0.024096178,-0.022766734,0.011217186,-0.003198975,0.007104218,-0.047195274,-0.013377533,0.013294443,0.024096178,-0.056501385,-0.026755067,-0.008433662,-0.001911076,0.007976666,-0.008101301,-0.014042255,0.008641388,-0.02176965,0.010012378,-0.000607598,-0.024927082,0.024927082,-0.018612221,-0.001184036,0.005567048,0.001324251,-0.019526213,-0.023597637,0.060489718,-0.010178559,-0.019609304,0.004112968,-0.011217186,-0.031574301,-0.008766023,0.005483958,-0.061819162,-0.023431456,-0.040714234,0.015039339,0.026422706,0.016202603,0.004653055,0.041046593,-0.018030589,0.040381871,-0.002638116,0.013045172,0.004216831,0.005650138,0.027419789,0.003926015,-0.028749233,0.004798463,-0.030244859,0.063813329,0.007145763,-0.017448956,0.025591804,-0.045201108,0.010718645,0.002804297,0.014291527,0.04586583,-0.015205519,-0.021603471,-0.035230275,0.00760276,0.033236109,0.016534964,-0.043206941,-0.003115885,-0.026256526,0.005940954,0.016534964,0.024262359,-0.001630647,0.028084511,-0.012795902,0.007270399,0.001381376,-0.009763107,-0.006896493,0.008433662,-0.019360034,0.000386889,0.030411039,0.025591804,0.010469374,0.037722982,-0.001147684,-0.005400868,0.052845411,-0.052513052,0.00768585,-0.004299921,0.00922302,0.011881908,0.012962082,-0.068798743,0.003593654,0.020938748,-0.013792985,-0.034565553,-0.007519669,-0.04021569,-0.020689478,0.006273315,0.046862911,0.006107135,0.002638116,-0.013792985,-0.005400868,-0.020274026,0.007644305,-0.010801735,0.026422706,0.043871664,0.003780607,0.010261648,-0.064145692,0.011881908,-0.009056839,0.009347656,-0.02459472,0.026422706,0.033236109,0.041212775,0.019027673,-0.00315743,0.004424557,0.020689478,-0.0153717,-0.015205519,-0.034897912,0.020274026,0.016867325,0.040714234,-0.022766734,-0.010967916,0.026256526,0.007062673,-0.015953332,-0.007727395,0.031574301,-0.002887387,-0.00614868,0.004569965,0.019027673,0.012878992,0.011798819,0.004258377,-0.019193852,-0.021437289,-0.021603471,0.000301202,-0.051183607,-0.004985416,-0.030078677,0.012629721,0.065142773,-0.031740483,-0.021104928,-0.03938479,-0.003365156,-0.016036423,0.036393538,0.009804652,-0.018612221,0.060489718,-0.003697517,0.000547876,0.063480966,0.02758597,0.010053922,-0.003655972,-0.001485239,0.018362951,0.021104928,-0.003905243,0.019443123,-0.002658889,-0.00380138,-0.013626805,0.035894997,0.035396457,-0.005691683,0.002762751,0.012878992,-0.009596926,-0.009970833,-0.015953332,0.022434372,0.00614868,-0.021188019,0.001557943,-0.020190936,0.009763107,0.017448956,0.006730312,0.005567048,0.019692395,-0.00218112,-0.016867325,0.006854947,0.007976666,0.019193852,0.040880412,0.007353489,-0.02127111,-0.031906664,-0.026755067,-0.017947499,0.040381871,0.042209856,0.00913993,-0.0307434,-0.017781317,-0.015039339,0.03057722,0.017532047,0.0187784,-0.060822077,0.002928932,-0.026422706,-0.005899409,0.039717149,0.026588887,-0.000971118,0.004923099,-0.013626805,0.0187784,-0.031408124,-0.000695881,0.050851244,-0.014457707,-0.007311944,-0.001293092,-0.002139574,-0.019276943,0.00290816,0.019360034,-0.017781317,0.002160347,0.016618054,-0.006522586,0.011798819,0.029247776,-0.02775215,0.010344739,-0.018362951,-0.036725901,-0.015870241,0.015704062,-0.012463541,0.02459472,-0.024096178,0.001152877,-0.031408124,0.025425622,0.027087428,0.00922302,0.034565553,0.015704062,-0.020689478,-0.00517237,-0.014706978,-0.001589101,0.026090344,0.014956249,0.011715728,0.004299921,-0.00913993,0.022434372,-0.03705826,0.048524719,-0.030411039,0.008433662,0.017033506,-0.000511525,-0.031408124,0.005940954,-0.012962082,-0.031574301,0.017448956,0.010178559,-0.011383367,-0.020107845,-0.005151597,0.006647222,0.013128263,0.007145763,0.008059756,-0.045201108,-0.004943871,0.015787151,-0.045201108,-0.020772567,-0.020274026,0.028250692,-0.024262359,-0.004424557,0.009804652,0.000472576,-0.005691683,0.001443693,-0.013294443,0.001412535,0.013211353,-0.01213118,-0.002118802,0.017781317,-0.007353489,-0.031075761,-0.004923099,0.011383367,-0.004486875,-0.010178559,0.016618054,0.014457707,0.023763817,-0.02459472,-0.00388447,0.012546631,-0.007519669,0.015704062,-0.014291527,0.009680017,-0.035562634,0.0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9 "numCandidates": 150, 10 "limit": 10, 11 "quantization": "scalar" 12 } 13 }, 14 { 15 "$project": { 16 "_id": 0, 17 "plot": 1, 18 "title": 1, 19 "score": { $meta: "vectorSearchScore" } 20 } 21 } 22 ]); 23 EOF 24 ) 25 26 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF 27 echo '${mdb_script}' > /tmp/mdb_script.js 28 mongosh --quiet "${MDB_CONNECTION_STRING}" < /tmp/mdb_script.js 29 EOF 30 )"
1 mdbc-rs [primary] test> switched to db sample_mflix 2 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [ 3 { 4 plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.', 5 title: 'About Time', 6 score: 0.7704131603240967 7 }, 8 { 9 plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', 10 title: 'Retroactive', 11 score: 0.7597770690917969 12 }, 13 { 14 plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', 15 title: 'Timecop', 16 score: 0.7574796676635742 17 }, 18 { 19 plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', 20 title: 'A.P.E.X.', 21 score: 0.7573235034942627 22 }, 23 { 24 plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.', 25 title: 'Back to the Future Part II', 26 score: 0.751945972442627 27 }, 28 { 29 plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', 30 title: 'Thrill Seekers', 31 score: 0.7503504753112793 32 }, 33 { 34 plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', 35 title: 'The Time Machine', 36 score: 0.750007152557373 37 }, 38 { 39 plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...', 40 title: 'Timerider: The Adventure of Lyle Swann', 41 score: 0.7499568462371826 42 }, 43 { 44 plot: 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.', 45 title: "The Time Traveler's Wife", 46 score: 0.7492842674255371 47 }, 48 { 49 plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', 50 title: 'The Final Countdown', 51 score: 0.7472751140594482 52 } 53 ] 54 mdbc-rs [primary] sample_mflix>
リソース
インデックスの作成と検索クエリの実行中の詳細については、以下を参照してください。