支持使用Kubernetes 操作符部署MongoDB Search 和MongoDB Vector Search 是作为 Preview功能提供的。在预览期间，功能和相应的文档可能随时更改。要学习；了解详情，请参阅 预览功能。
如果成功安装了 MongoDBSearch资源，则可以将MongoDB Search 和 Vector Search 索引创建为 mdb-user，并使用索引运行MongoDB Search 和 Vector Search 查询。本页介绍如何加载示例数据、为数据创建MongoDB Search 和 Vector Search 索引，以及对数据运行MongoDB Search 和 Vector Search 查询。
先决条件
要使用MongoDB Search 和 Vector Search，您必须具备以下条件：
成功部署
MongoDB和MongoDBSearch。要了解详情，请参阅：
有权创建MongoDB搜索和向量搜索以及运行MongoDB搜索和向量搜索查询的用户。
要学习；了解更多信息，请参阅为MongoDB Search 和 Vector Search 配置Cloud Manager或MongoDB Ops Manager用户。
Kubernetes命令行工具
kubectl，配置为与集群通信。Bash v5.1 或更高版本，用于运行本教程中的命令。
步骤
1
设置环境变量。
要设立用于此过程后续步骤的环境变量，请复制以下内容，设立环境变量的值，然后加载环境变量：
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=${MDB_RESOURCE_NAME}"
2
部署MongoDB工具 Pod。
部署名为 mongodb-tools-pod 的 Pod，以从Kubernetes集群中运行mongosh 和 mongorestore 等MongoDB客户端工具。在集群内运行 Pod 可简化与MongoDB的连接，而无需向外部公开数据库。
以下命令部署一个名为 mongodb-tools-pod 且运行MongoDB Community Server 映像的 Pod。使用 sleep infinity 命令让 Pod 保持运行，以便您使用 kubectl exec运行MongoDB客户端工具。
1 kubectl apply -n "${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
导入示例数据。
导入 sample_mflix数据库，其中包含以下集合：
集合名称 | 说明 |
|---|---|
| 包含与特定电影相关的评论。 |
| 包含
|
| 包含电影信息，其中包括发行年份、导演和评价。 |
| 元数据字段。包含用户的JSON Web 令牌。 |
| 包含电影院的位置。 |
| 包含用户信息。 |
以下命令下载示例数据集，并使用 mongorestore 将数据加载到MongoDB 部署中的 sample_mflix数据库，以 mdb-user 身份进行连接。
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
创建索引。
您可以创建MongoDB Search 或 Vector Search索引。您必须创建MongoDB搜索索引来运行$search 和 $searchMeta 查询，以及向量搜索索引来运行$vectorSearch 查询。
MongoDB Search 提供灵活的索引定义，支持动态和静态字段映射、各种分析器（标准、特定语言、自定义）以及同义词和分面搜索等功能。要学习；了解更多信息，请参阅定义索引。
Vector Search 提供简单的索引定义，支持使用 ANN 和 ENN 搜索算法根据语义相似性查询数据中的向量嵌入。要学习；了解更多信息，请参阅定义向量搜索索引。
在终端中复制、粘贴并运行以下命令，以创建MongoDB Search 或 Vector Search索引。
该命令使用
kubectl exec运行mongodb-tools-pod中的mongosh。它连接到sample_mflixdb.<collection>.createSearchIndex()方法以创建索引。以下命令使用
movies集合上的动态映射创建名为default的MongoDB搜索索引。动态映射会自动对所有可动态索引的字段类型进行索引。1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \ 2 mongosh --quiet \ 3 "${MDB_CONNECTION_STRING}" \ 4 --eval "use sample_mflix" \ 5 --eval 'db.movies.createSearchIndex("default", { mappings: { dynamic: true } });' 以下命令在
embedded_movies集合中具有2048个维度的plot_embedding_voyage_3_large字段上创建名为vector_index的向量搜索索引，用于运行ANN 查询。1 kubectl exec --context "${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 } ] });' 等待索引创建完成。
索引创建是一个异步进程。运行以下脚本以等待 60 秒以完成索引创建。
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 或者，运行以下命令来检索集合的索引列表。
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
使用索引查询数据。
索引创建成功后，您可以使用该索引运行MongoDB Search 或 Vector Search 查询。您还可以将MongoDB Search 与 Vector Search 结合使用。要学习；了解有关运行MongoDB Search 查询的更多信息，请参阅定义查询。要学习；了解有关向量搜索查询的更多信息，请参阅运行向量搜索查询。
以下脚本定义聚合管道查询条件，并使用 kubectl exec 命令加载 mongosh 以执行查询。
以下查询在 plot字段中搜索包含术语baseball 的电影情节。它不包括 comedy 和 romance 类型的电影。
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>
以下查询使用 2048 维度向量嵌入在 plot_embedding_voyage_3_large字段中搜索字符串 time travel，而该字符串是使用 Voyage AI 的 voyage-3-large 模型生成的。
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>
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