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Deploy Search & Vector Search

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Management

Controllers for Kubernetes Operator

Deploy Search & Vector Search

Docs Home

Management

Controllers for Kubernetes Operator

Deploy Search & Vector Search

Use MongoDB Search and Vector Search

After you install the MongoDBSearch custom resource, you can create MongoDB Search and Vector Search indexes as the mdb-user and run MongoDB Search and Vector Search queries using the indexes. This page describes how to load sample data, create MongoDB Search and Vector Search indexes for the sample data, and run MongoDB Search and Vector Search queries against the sample data.

Prerequisites

To use MongoDB Search and Vector Search, you must have the following:

Successful deployment of MongoDB and MongoDBSearch.

To learn more, see:
- Deploy MongoDB Search and Vector Search
User with permissions to create MongoDB Search and Vector Search indexes and run MongoDB Search and Vector Search queries.

To learn more, see Configure a User for MongoDB Search and Vector Search.
kubectl, configured to communicate with your cluster.
Bash v5.1 or later to run the commands in this tutorial.

Procedure

Set the environment variables.

To set the environment variables for use in the subsequent steps in this procedure, copy the following, set the values for the environment variables, and then load the environment variables:

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 # TLS-related resources used by the snippets in this module
19 #export MDB_TLS_CA_SECRET_NAME="${MDB_RESOURCE_NAME}-ca"
20 #export MDB_TLS_CA_CONFIGMAP="${MDB_RESOURCE_NAME}-ca-configmap"
21 #export MDB_TLS_SERVER_CERT_SECRET_NAME="${MDB_RESOURCE_NAME}-tls"
22 #export MDB_SEARCH_TLS_SECRET_NAME="${MDB_RESOURCE_NAME}-search-tls"
23 
24 # default connection string if MongoDB database is deployed using the operator
25 #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=${MDB_RESOURCE_NAME}"
26 # To enable TLS for the shared snippets, append driver options directly to the connection string, for example:
27 #export MDB_CONNECTION_STRING="mongodb://mdb-user:${MDB_USER_PASSWORD}@${MDB_RESOURCE_NAME}-0.${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local:27017/?replicaSet=${MDB_RESOURCE_NAME}&tls=true&tlsCAFile=/tls/ca.crt"

To connect using the TLS-enabled connection string, as shown (on line 27) in the env_variables.sh file, download the CA certificate for the MongoDBSearch service so that the mongosh client can reference it.

Deploy a pod for MongoDB client tools.

Deploy a pod named mongodb-tools-pod to run MongoDB client tools like mongosh and mongorestore from within the Kubernetes cluster. Running a pod inside the cluster simplifies connectivity to MongoDB without needing to expose the database externally.

The following command deploys a pod that runs a MongoDB Community Server image, which ships with mongosh. The pod is kept running with a sleep infinity command so that you can use kubectl exec to run MongoDB client tools against your actual cluster.

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%-ent}-ubi8
12     command: ["/bin/bash", "-c"]
13     args: ["sleep infinity"]
14     volumeMounts:
15     - name: mongo-ca
16       mountPath: /tls
17       readOnly: true
18   restartPolicy: Never
19   volumes:
20   - name: mongo-ca
21     configMap:
22       name: ${MDB_TLS_CA_CONFIGMAP:-"${MDB_RESOURCE_NAME}-ca-configmap"}
23       optional: true
24       items:
25       - key: ca.crt
26         path: ca.crt
27 EOF
28 
29 echo "Waiting for the mongodb-tools to be ready..."
30 kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait --for=condition=Ready pod/mongodb-tools-pod --timeout=60s

Import the sample data.

Import the sample_mflix database, which contains the following collections:

Collection Name	Description
`comments`	Contains comments associated with specific movies.
`embedded_movies`	Contains details on movies in the `Western`, `Action`, and `Fantasy` genres from the `movies` collection with the following additional fields: `plot_embedding_voyage_3_large` - Contains 2048d embeddings created from the `plot` field using Voyage AI's `voyage-3-large` embedding model and converted to `binData` for efficient storage and retrieval. `plot_embedding` - Contains 1536d embeddings created from the `plot` field using OpenAI's `text-embedding-ada-002` embedding model and converted to `binData` for efficient storage and retrieval.
`movies`	Contains movie information, including release year, director, and reviews.
`sessions`	Metadata field. Contains users' JSON Web Tokens.
`theaters`	Contains locations of movie theaters.
`users`	Contains user information.

The following command downloads the sample dataset and uses mongorestore to load the data into the sample_mflix database in your MongoDB deployment, connecting as the mdb-user.

1 echo "Downloading sample database archive..."
2 kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" mongodb-tools-pod -- \
3   curl -fSL https://atlas-education.s3.amazonaws.com/sample_mflix.archive \
4   -o /tmp/sample_mflix.archive
5 
6 echo "Restoring sample database..."
7 kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" mongodb-tools-pod -- \
8   mongorestore \
9     --archive=/tmp/sample_mflix.archive \
10     --verbose=1 \
11     --drop \
12     --nsInclude 'sample_mflix.*' \
13     --uri="${MDB_CONNECTION_STRING}"

Create an index.

You can create a MongoDB Search or a Vector Search index. You must create a MongoDB Search index to run $search and $searchMeta queries and Vector Search index to run $vectorSearch queries.

MongoDB Search provides flexible index definitions that support dynamic and static field mappings, various analyzers (standard, language-specific, custom), and features like synonyms and faceted search. To learn more, see Define Your Index.
Vector Search provides simple index definition that supports querying vector embeddings or text (if you enabled mongot for Automated Embedding) in your data based on semantic similarity by using ANN and ENN search algorithm. To learn more, see Define Vector Search Index.

Copy, paste, and run the following command in your terminal to create a MongoDB Search or a Vector Search index.

The command uses kubectl exec to run mongosh in the mongodb-tools-pod. It connects to the sample_mflix database and uses the db.<collection>.createSearchIndex() method to create an index.

The following command creates a MongoDB Search index named default with dynamic mappings on the movies collection. Dynamic mapping automatically indexes all dynamically indexable field types.

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.movies.createSearchIndex("default", { mappings: { dynamic: true } });'

The following command creates a Vector Search index named vector_index on the plot_embedding_voyage_3_large field with 2048 vector dimensions in the embedded_movies collection for running ANN queries.

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     } ] });'

You can create the following index only if you configured mongot for Automated Embedding. Automated Embedding is available as a Preview feature for MongoDB Community Edition.

The following command creates a Vector Search index named vector_auto_embed_index on the plot field in the movies collection. The index definition uses the embedding model set in the environment variable for generating embeddings for the data in the plot field.

1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
2   mongosh --quiet "${MDB_CONNECTION_STRING}" \
3     --eval "use sample_mflix" \
4     --eval 'db.movies.createSearchIndex("vector_auto_embed_index", "vectorSearch",
5       { "fields": [ {
6         "type": "autoEmbed",
7         "modality": "text",
8         "path": "plot",
9         "model": "'"${EMBEDDING_MODEL}"'"
10       } ] });'

Verify that the index creation succeeded.

Run the following command to retrieve the list of indexes for the collection.

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"});'

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 {
2   cursor: {
3     id: 0,
4     ns: 'sample_mflix.movies',
5     firstBatch: [
6       {
7         id: '6abb9dd880557b2f2227865f',
8         name: 'default',
9         status: 'READY',
10         queryable: true,
11         latestDefinitionVersion: { version: 0, createdAt: ISODate('2026-09-29T11:15:36.000Z') },
12         latestDefinition: { mappings: { dynamic: true } },
13         statusDetail: [
14           {
15             hostname: 'mdbc-rs-search-0-0.6abb9ca688d67b6ecf5a64f6',
16             status: 'READY',
17             queryable: true,
18             mainIndex: {
19               status: 'READY',
20               queryable: true,
21               definitionVersion: {
22                 version: 0,
23                 createdAt: ISODate('2026-09-29T11:15:36.000Z')
24               },
25               definition: { mappings: { dynamic: true, fields: {} } }
26             }
27           }
28         ]
29       },
30       {
31         id: '6abb9dd980557b2f22278661',
32         name: 'vector_auto_embed_index',
33         status: 'READY',
34         queryable: true,
35         latestDefinitionVersion: { version: 0, createdAt: ISODate('2026-09-29T11:15:37.000Z') },
36         latestDefinition: {
37           fields: [
38             {
39               type: 'autoEmbed',
40               modality: 'text',
41               path: 'plot',
42               model: 'voyage-4'
43             }
44           ]
45         },
46         statusDetail: [
47           {
48             hostname: 'mdbc-rs-search-0-0.6abb9ca688d67b6ecf5a64f6',
49             status: 'READY',
50             queryable: true,
51             mainIndex: {
52               status: 'READY',
53               queryable: true,
54               definitionVersion: {
55                 version: 0,
56                 createdAt: ISODate('2026-09-29T11:15:37.000Z')
57               },
58               definition: {
59                 fields: [
60                   {
61                     type: 'autoEmbed',
62                     path: 'plot',
63                     numDimensions: 1024,
64                     similarity: 'dotProduct',
65                     quantization: 'float',
66                     model: 'voyage-4',
67                     modality: 'text'
68                   }
69                 ]
70               }
71             }
72           }
73         ]
74       }
75     ]
76   },
77   ok: 1,
78   '$clusterTime': {
79     clusterTime: Timestamp({ t: 1790680626, i: 1 }),
80     signature: {
81       hash: Binary.createFromBase64('4x00PUrbiAWIbepjvexGKb/uA9Q=', 0),
82       keyId: Long('7690912939544412168')
83     }
84   },
85   operationTime: Timestamp({ t: 1790680626, i: 1 })
86 }

Query the data using the index.

After the index creation is successful, you can run MongoDB Search or Vector Search queries using the index. You can also combine MongoDB Search with Vector Search. To learn more about running MongoDB Search queries, see Define Your Query. To learn more about Vector Search queries, see Run Vector Search ANN and ENN Queries.

The following script defines the aggregation pipeline query criteria and uses the kubectl exec command to load mongosh to execute the query.

The following query searches the plot field for movie plots that contain the term baseball. It excludes movies in the comedy and romance genres.

1 kubectl exec -i --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
2   mongosh --quiet "${MDB_CONNECTION_STRING}" <<'EOF'
3 use sample_mflix;
4 db.movies.aggregate([
5   {
6     $search: {
7       "compound": {
8         "must": [ {
9           "text": {
10             "query": "baseball",
11             "path": "plot"
12           }
13         }],
14         "mustNot": [ {
15           "text": {
16             "query": ["Comedy", "Romance"],
17             "path": "genres"
18           }
19         } ]
20       },
21       "sort": {
22         "released": -1
23       }
24     }
25   },
26   {
27     $limit: 3
28   },
29   {
30     $project: {
31       "_id": 0,
32       "title": 1,
33       "plot": 1,
34       "genres": 1,
35       "released": 1
36     }
37   }
38 ]);
39 EOF

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>

The following query searches the plot_embedding_voyage_3_large field using 2048 dimension vector embeddings for the string time travel, generated using Voyage AI's voyage-3-large model.

1 kubectl exec -i --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
2   mongosh --quiet "${MDB_CONNECTION_STRING}" <<'EOF'
3 use sample_mflix;
4 
5 const sourceMovieTitle = "Back to the Future Part II";
6 const sourceMovie = db.embedded_movies.findOne(
7   {
8     title: sourceMovieTitle,
9     plot_embedding_voyage_3_large: { $exists: true }
10   },
11   { plot_embedding_voyage_3_large: 1 }
12 );
13 if (!sourceMovie) {
14   throw new Error(`No source movie named "${sourceMovieTitle}" contains plot_embedding_voyage_3_large`);
15 }
16 print(`Finding movies with plots similar to "${sourceMovieTitle}"`);
17 const queryVector = sourceMovie.plot_embedding_voyage_3_large;
18 
19 db.embedded_movies.aggregate([
20   {
21     $vectorSearch: {
22       index: "vector_index",
23       path: "plot_embedding_voyage_3_large",
24       queryVector,
25       numCandidates: 100,
26       limit: 6
27     }
28   },
29   { $match: { _id: { $ne: sourceMovie._id } } },
30   { $limit: 5 },
31   {
32     $project: {
33       _id: 0,
34       title: 1,
35       year: 1,
36       plot: 1,
37       score: { $meta: "vectorSearchScore" }
38     }
39   }
40 ]);
41 EOF

1 mdbc-rs [primary] test> switched to db sample_mflix
2 mdbc-rs [primary] sample_mflix> 
3 mdbc-rs [primary] sample_mflix> 
4 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... 
5 mdbc-rs [primary] sample_mflix> ... ... 
6 mdbc-rs [primary] sample_mflix> Finding movies with plots similar to "Back to the Future Part II"
7 
8 mdbc-rs [primary] sample_mflix> 
9 mdbc-rs [primary] sample_mflix> 
10 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
11   {
12     year: 1990,
13     plot: 'Enjoying a peaceable existence in 1885, Doctor Emmet Brown is about to be killed by Buford "Mad Dog" Tannen. Marty McFly travels back in time to save his friend.',
14     title: 'Back to the Future Part III',
15     score: 0.9355776309967041
16   },
17   {
18     year: 2015,
19     plot: 'When John Connor, leader of the human resistance, sends Sgt. Kyle Reese back to 1984 to protect Sarah Connor and safeguard the future, an unexpected turn of events creates a fractured timeline.',
20     title: 'Terminator Genisys',
21     score: 0.9031102657318115
22   },
23   {
24     plot: 'A tyrant from the future creates evil android doubles of Bill and Ted and sends them back to eliminate the originals.',
25     title: "Bill & Ted's Bogus Journey",
26     year: 1991,
27     score: 0.9027583599090576
28   },
29   {
30     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...',
31     title: 'A.P.E.X.',
32     year: 1994,
33     score: 0.8977663516998291
34   },
35   {
36     plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
37     title: 'Love Story 2050',
38     year: 2008,
39     score: 0.8973734378814697
40   }
41 ]
42 mdbc-rs [primary] sample_mflix>

The following query searches the plot field for movies with plots that are semantically similar to spy thriller, for which Vector Search automatically generated embeddings using the model specified in the index for the plot field (voyage-4). The query specifies up to 150 neighbors to consider and returns only the top 10 documents in the results.

1 kubectl exec -i --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
2   mongosh --quiet "${MDB_CONNECTION_STRING}" <<'EOF'
3 use sample_mflix;
4 db.movies.aggregate([
5    {
6      "$vectorSearch": {
7        "index": "vector_auto_embed_index",
8        "path": "plot",
9        "query": "spy thriller",
10        "numCandidates": 150,
11        "limit": 10
12      }
13    },
14    {
15      "$project": {
16        "_id": 0,
17        "plot": 1,
18        "title": 1,
19        "score": { "$meta": "vectorSearchScore" }
20      }
21    }
22  ]);
23 EOF

1 mdbc-rs [primary] test> switched to db sample_mflix
2 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
3   {
4     plot: 'A highly intellectual but socially awkward spy is tasked with preventing a terrorist attack from a Russian spy agency.',
5     title: 'Get Smart',
6     score: 0.8280618190765381
7   },
8   {
9     plot: 'A dramatization of the missions and adventures of the greatest spy in British history.',
10     title: 'Reilly: Ace of Spies',
11     score: 0.8077303767204285
12   },
13   {
14     plot: 'An American agent, under false suspicion of disloyalty, must discover and expose the real spy without the help of his organization.',
15     title: 'Mission: Impossible',
16     score: 0.8074153661727905
17   },
18   {
19     plot: 'A researcher for the CIA who convinces his superiors to send him to the eastern bloc in order to avenge the murder of his wife by enemy agents discovers a web of deception underneath his ...',
20     title: 'The Amateur',
21     score: 0.8071699738502502
22   },
23   {
24     plot: 'A French intelligence agent becomes embroiled in the Cold War politics first with uncovering the events leading up to the 1962 Cuban Missle Crisis, and then back to France to break up an international Russian spy ring.',
25     title: 'Topaz',
26     score: 0.8057430982589722
27   },
28   {
29     plot: 'A bookish CIA researcher finds all his co-workers dead, and must outwit those responsible until he figures out who he can really trust.',
30     title: 'Three Days of the Condor',
31     score: 0.8037759065628052
32   },
33   {
34     plot: 'An unsuspecting, disenchanted man finds himself working as a spy in the dangerous, high-stakes world of corporate espionage. Quickly getting way over-his-head, he teams up with a mysterious femme fatale.',
35     title: 'Cypher',
36     score: 0.8033789396286011
37   },
38   {
39     plot: 'Two ex-government agents turned rival industrial spies have to be at the top of their game when one of their companies prepares to launch a major product. However, they distract each other in more ways than one.',
40     title: 'Duplicity',
41     score: 0.8026910424232483
42   },
43   {
44     plot: 'A CIA agent goes on the run after a defector accuses her of being a Russian spy.',
45     title: 'Salt',
46     score: 0.8025847673416138
47   },
48   {
49     plot: 'In the bleak days of the Cold War, espionage veteran George Smiley is forced from semi-retirement to uncover a Soviet agent within MI6.',
50     title: 'Tinker Tailor Soldier Spy',
51     score: 0.8008040189743042
52   }
53 ]
54 mdbc-rs [primary] sample_mflix>

Set the environment variables.

To set the environment variables for use in the subsequent steps in this procedure, copy the following, set the values for the environment variables, and then load the environment variables:

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 # TLS-related resources used by the snippets in this module
19 #export MDB_TLS_CA_SECRET_NAME="${MDB_RESOURCE_NAME}-ca"
20 #export MDB_TLS_CA_CONFIGMAP="${MDB_RESOURCE_NAME}-ca-configmap"
21 #export MDB_TLS_SERVER_CERT_SECRET_NAME="${MDB_RESOURCE_NAME}-tls"
22 #export MDB_SEARCH_TLS_SECRET_NAME="${MDB_RESOURCE_NAME}-search-tls"
23 
24 # default connection string if MongoDB database is deployed using the operator
25 #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=${MDB_RESOURCE_NAME}"
26 # To enable TLS for the shared snippets, append driver options directly to the connection string, for example:
27 #export MDB_CONNECTION_STRING="mongodb://mdb-user:${MDB_USER_PASSWORD}@${MDB_RESOURCE_NAME}-0.${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local:27017/?replicaSet=${MDB_RESOURCE_NAME}&tls=true&tlsCAFile=/tls/ca.crt"

To connect using the TLS-enabled connection string, as shown (on line 27) in the env_variables.sh file, download the CA certificate for the MongoDBSearch service so that the mongosh client can reference it.

Deploy a pod for MongoDB client tools.

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%-ent}-ubi8
12     command: ["/bin/bash", "-c"]
13     args: ["sleep infinity"]
14     volumeMounts:
15     - name: mongo-ca
16       mountPath: /tls
17       readOnly: true
18   restartPolicy: Never
19   volumes:
20   - name: mongo-ca
21     configMap:
22       name: ${MDB_TLS_CA_CONFIGMAP:-"${MDB_RESOURCE_NAME}-ca-configmap"}
23       optional: true
24       items:
25       - key: ca.crt
26         path: ca.crt
27 EOF
28 
29 echo "Waiting for the mongodb-tools to be ready..."
30 kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait --for=condition=Ready pod/mongodb-tools-pod --timeout=60s

Import the sample data.

Import the sample_mflix database, which contains the following collections:

Collection Name	Description
`comments`	Contains comments associated with specific movies.
`embedded_movies`	Contains details on movies in the `Western`, `Action`, and `Fantasy` genres from the `movies` collection with the following additional fields: `plot_embedding_voyage_3_large` - Contains 2048d embeddings created from the `plot` field using Voyage AI's `voyage-3-large` embedding model and converted to `binData` for efficient storage and retrieval. `plot_embedding` - Contains 1536d embeddings created from the `plot` field using OpenAI's `text-embedding-ada-002` embedding model and converted to `binData` for efficient storage and retrieval.
`movies`	Contains movie information, including release year, director, and reviews.
`sessions`	Metadata field. Contains users' JSON Web Tokens.
`theaters`	Contains locations of movie theaters.
`users`	Contains user information.

The following command downloads the sample dataset and uses mongorestore to load the data into the sample_mflix database in your MongoDB deployment, connecting as the mdb-user.

1 echo "Downloading sample database archive..."
2 kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" mongodb-tools-pod -- \
3   curl -fSL https://atlas-education.s3.amazonaws.com/sample_mflix.archive \
4   -o /tmp/sample_mflix.archive
5 
6 echo "Restoring sample database..."
7 kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" mongodb-tools-pod -- \
8   mongorestore \
9     --archive=/tmp/sample_mflix.archive \
10     --verbose=1 \
11     --drop \
12     --nsInclude 'sample_mflix.*' \
13     --uri="${MDB_CONNECTION_STRING}"

Create an index.

You can create a MongoDB Search or a Vector Search index. You must create a MongoDB Search index to run $search and $searchMeta queries and Vector Search index to run $vectorSearch queries.

MongoDB Search provides flexible index definitions that support dynamic and static field mappings, various analyzers (standard, language-specific, custom), and features like synonyms and faceted search. To learn more, see Define Your Index.
Vector Search provides simple index definition that supports querying vector embeddings or text (if you enabled mongot for Automated Embedding) in your data based on semantic similarity by using ANN and ENN search algorithm. To learn more, see Define Vector Search Index.

Copy, paste, and run the following command in your terminal to create a MongoDB Search or a Vector Search index.

The command uses kubectl exec to run mongosh in the mongodb-tools-pod. It connects to the sample_mflix database and uses the db.<collection>.createSearchIndex() method to create an index.

The following command creates a MongoDB Search index named default with dynamic mappings on the movies collection. Dynamic mapping automatically indexes all dynamically indexable field types.

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.movies.createSearchIndex("default", { mappings: { dynamic: true } });'

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     } ] });'

You can create the following index only if you configured mongot for Automated Embedding. Automated Embedding is available as a Preview feature for MongoDB Community Edition.

1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
2   mongosh --quiet "${MDB_CONNECTION_STRING}" \
3     --eval "use sample_mflix" \
4     --eval 'db.movies.createSearchIndex("vector_auto_embed_index", "vectorSearch",
5       { "fields": [ {
6         "type": "autoEmbed",
7         "modality": "text",
8         "path": "plot",
9         "model": "'"${EMBEDDING_MODEL}"'"
10       } ] });'

Verify that the index creation succeeded.

Run the following command to retrieve the list of indexes for the collection.

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"});'

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 {
2   cursor: {
3     id: 0,
4     ns: 'sample_mflix.movies',
5     firstBatch: [
6       {
7         id: '6abb9dd880557b2f2227865f',
8         name: 'default',
9         status: 'READY',
10         queryable: true,
11         latestDefinitionVersion: { version: 0, createdAt: ISODate('2026-09-29T11:15:36.000Z') },
12         latestDefinition: { mappings: { dynamic: true } },
13         statusDetail: [
14           {
15             hostname: 'mdbc-rs-search-0-0.6abb9ca688d67b6ecf5a64f6',
16             status: 'READY',
17             queryable: true,
18             mainIndex: {
19               status: 'READY',
20               queryable: true,
21               definitionVersion: {
22                 version: 0,
23                 createdAt: ISODate('2026-09-29T11:15:36.000Z')
24               },
25               definition: { mappings: { dynamic: true, fields: {} } }
26             }
27           }
28         ]
29       },
30       {
31         id: '6abb9dd980557b2f22278661',
32         name: 'vector_auto_embed_index',
33         status: 'READY',
34         queryable: true,
35         latestDefinitionVersion: { version: 0, createdAt: ISODate('2026-09-29T11:15:37.000Z') },
36         latestDefinition: {
37           fields: [
38             {
39               type: 'autoEmbed',
40               modality: 'text',
41               path: 'plot',
42               model: 'voyage-4'
43             }
44           ]
45         },
46         statusDetail: [
47           {
48             hostname: 'mdbc-rs-search-0-0.6abb9ca688d67b6ecf5a64f6',
49             status: 'READY',
50             queryable: true,
51             mainIndex: {
52               status: 'READY',
53               queryable: true,
54               definitionVersion: {
55                 version: 0,
56                 createdAt: ISODate('2026-09-29T11:15:37.000Z')
57               },
58               definition: {
59                 fields: [
60                   {
61                     type: 'autoEmbed',
62                     path: 'plot',
63                     numDimensions: 1024,
64                     similarity: 'dotProduct',
65                     quantization: 'float',
66                     model: 'voyage-4',
67                     modality: 'text'
68                   }
69                 ]
70               }
71             }
72           }
73         ]
74       }
75     ]
76   },
77   ok: 1,
78   '$clusterTime': {
79     clusterTime: Timestamp({ t: 1790680626, i: 1 }),
80     signature: {
81       hash: Binary.createFromBase64('4x00PUrbiAWIbepjvexGKb/uA9Q=', 0),
82       keyId: Long('7690912939544412168')
83     }
84   },
85   operationTime: Timestamp({ t: 1790680626, i: 1 })
86 }

Query the data using the index.

The following script defines the aggregation pipeline query criteria and uses the kubectl exec command to load mongosh to execute the query.

The following query searches the plot field for movie plots that contain the term baseball. It excludes movies in the comedy and romance genres.

1 kubectl exec -i --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
2   mongosh --quiet "${MDB_CONNECTION_STRING}" <<'EOF'
3 use sample_mflix;
4 db.movies.aggregate([
5   {
6     $search: {
7       "compound": {
8         "must": [ {
9           "text": {
10             "query": "baseball",
11             "path": "plot"
12           }
13         }],
14         "mustNot": [ {
15           "text": {
16             "query": ["Comedy", "Romance"],
17             "path": "genres"
18           }
19         } ]
20       },
21       "sort": {
22         "released": -1
23       }
24     }
25   },
26   {
27     $limit: 3
28   },
29   {
30     $project: {
31       "_id": 0,
32       "title": 1,
33       "plot": 1,
34       "genres": 1,
35       "released": 1
36     }
37   }
38 ]);
39 EOF

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>

The following query searches the plot_embedding_voyage_3_large field using 2048 dimension vector embeddings for the string time travel, generated using Voyage AI's voyage-3-large model.

1 kubectl exec -i --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
2   mongosh --quiet "${MDB_CONNECTION_STRING}" <<'EOF'
3 use sample_mflix;
4 
5 const sourceMovieTitle = "Back to the Future Part II";
6 const sourceMovie = db.embedded_movies.findOne(
7   {
8     title: sourceMovieTitle,
9     plot_embedding_voyage_3_large: { $exists: true }
10   },
11   { plot_embedding_voyage_3_large: 1 }
12 );
13 if (!sourceMovie) {
14   throw new Error(`No source movie named "${sourceMovieTitle}" contains plot_embedding_voyage_3_large`);
15 }
16 print(`Finding movies with plots similar to "${sourceMovieTitle}"`);
17 const queryVector = sourceMovie.plot_embedding_voyage_3_large;
18 
19 db.embedded_movies.aggregate([
20   {
21     $vectorSearch: {
22       index: "vector_index",
23       path: "plot_embedding_voyage_3_large",
24       queryVector,
25       numCandidates: 100,
26       limit: 6
27     }
28   },
29   { $match: { _id: { $ne: sourceMovie._id } } },
30   { $limit: 5 },
31   {
32     $project: {
33       _id: 0,
34       title: 1,
35       year: 1,
36       plot: 1,
37       score: { $meta: "vectorSearchScore" }
38     }
39   }
40 ]);
41 EOF

1 mdbc-rs [primary] test> switched to db sample_mflix
2 mdbc-rs [primary] sample_mflix> 
3 mdbc-rs [primary] sample_mflix> 
4 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... 
5 mdbc-rs [primary] sample_mflix> ... ... 
6 mdbc-rs [primary] sample_mflix> Finding movies with plots similar to "Back to the Future Part II"
7 
8 mdbc-rs [primary] sample_mflix> 
9 mdbc-rs [primary] sample_mflix> 
10 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
11   {
12     year: 1990,
13     plot: 'Enjoying a peaceable existence in 1885, Doctor Emmet Brown is about to be killed by Buford "Mad Dog" Tannen. Marty McFly travels back in time to save his friend.',
14     title: 'Back to the Future Part III',
15     score: 0.9355776309967041
16   },
17   {
18     year: 2015,
19     plot: 'When John Connor, leader of the human resistance, sends Sgt. Kyle Reese back to 1984 to protect Sarah Connor and safeguard the future, an unexpected turn of events creates a fractured timeline.',
20     title: 'Terminator Genisys',
21     score: 0.9031102657318115
22   },
23   {
24     plot: 'A tyrant from the future creates evil android doubles of Bill and Ted and sends them back to eliminate the originals.',
25     title: "Bill & Ted's Bogus Journey",
26     year: 1991,
27     score: 0.9027583599090576
28   },
29   {
30     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...',
31     title: 'A.P.E.X.',
32     year: 1994,
33     score: 0.8977663516998291
34   },
35   {
36     plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
37     title: 'Love Story 2050',
38     year: 2008,
39     score: 0.8973734378814697
40   }
41 ]
42 mdbc-rs [primary] sample_mflix>

1 kubectl exec -i --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
2   mongosh --quiet "${MDB_CONNECTION_STRING}" <<'EOF'
3 use sample_mflix;
4 db.movies.aggregate([
5    {
6      "$vectorSearch": {
7        "index": "vector_auto_embed_index",
8        "path": "plot",
9        "query": "spy thriller",
10        "numCandidates": 150,
11        "limit": 10
12      }
13    },
14    {
15      "$project": {
16        "_id": 0,
17        "plot": 1,
18        "title": 1,
19        "score": { "$meta": "vectorSearchScore" }
20      }
21    }
22  ]);
23 EOF

1 mdbc-rs [primary] test> switched to db sample_mflix
2 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
3   {
4     plot: 'A highly intellectual but socially awkward spy is tasked with preventing a terrorist attack from a Russian spy agency.',
5     title: 'Get Smart',
6     score: 0.8280618190765381
7   },
8   {
9     plot: 'A dramatization of the missions and adventures of the greatest spy in British history.',
10     title: 'Reilly: Ace of Spies',
11     score: 0.8077303767204285
12   },
13   {
14     plot: 'An American agent, under false suspicion of disloyalty, must discover and expose the real spy without the help of his organization.',
15     title: 'Mission: Impossible',
16     score: 0.8074153661727905
17   },
18   {
19     plot: 'A researcher for the CIA who convinces his superiors to send him to the eastern bloc in order to avenge the murder of his wife by enemy agents discovers a web of deception underneath his ...',
20     title: 'The Amateur',
21     score: 0.8071699738502502
22   },
23   {
24     plot: 'A French intelligence agent becomes embroiled in the Cold War politics first with uncovering the events leading up to the 1962 Cuban Missle Crisis, and then back to France to break up an international Russian spy ring.',
25     title: 'Topaz',
26     score: 0.8057430982589722
27   },
28   {
29     plot: 'A bookish CIA researcher finds all his co-workers dead, and must outwit those responsible until he figures out who he can really trust.',
30     title: 'Three Days of the Condor',
31     score: 0.8037759065628052
32   },
33   {
34     plot: 'An unsuspecting, disenchanted man finds himself working as a spy in the dangerous, high-stakes world of corporate espionage. Quickly getting way over-his-head, he teams up with a mysterious femme fatale.',
35     title: 'Cypher',
36     score: 0.8033789396286011
37   },
38   {
39     plot: 'Two ex-government agents turned rival industrial spies have to be at the top of their game when one of their companies prepares to launch a major product. However, they distract each other in more ways than one.',
40     title: 'Duplicity',
41     score: 0.8026910424232483
42   },
43   {
44     plot: 'A CIA agent goes on the run after a defector accuses her of being a Russian spy.',
45     title: 'Salt',
46     score: 0.8025847673416138
47   },
48   {
49     plot: 'In the bleak days of the Cold War, espionage veteran George Smiley is forced from semi-retirement to uncover a Soviet agent within MI6.',
50     title: 'Tinker Tailor Soldier Spy',
51     score: 0.8008040189743042
52   }
53 ]
54 mdbc-rs [primary] sample_mflix>

Set the environment variables.

To set the environment variables for use in the subsequent steps in this procedure, copy the following, uncomment the variables, replace the placeholder values with your deployment details, and then load the environment variables:

1 # The env vars here are all commented out because this
2 # snippets module is reusable across different sharded
3 # MongoDB Search deployments and all the necessary
4 # variables should be already defined there.
5 #
6 # Required env var contract:
7 #   MDB_ADMIN_CONNECTION_STRING
8 #     — for import/sharding commands (admin privileges)
9 #   MDB_USER_CONNECTION_STRING
10 #     — for search queries (regular user privileges)
11 #   MDB_CONNECTION_STRING
12 #     — fallback if the above are not set
13 #       (e.g., non-sharded usage)
14 #   K8S_CTX    — Kubernetes context
15 #   MDB_NS     — Kubernetes namespace
16 #   MDB_VERSION
17 #     — MongoDB version (e.g., 8.2.6-ent)
18 #   MDB_TLS_CA_CONFIGMAP
19 #     — CA certificate ConfigMap name for tools pod
20 #       TLS mount
21 
22 # set it to the context name of the k8s cluster
23 #export K8S_CTX="<local cluster context>"
24 # the following namespace will be used to deploy
25 # mongodb tools pod
26 #export MDB_NS="mongodb"
27 
28 # MongoDB version (used for tools pod image)
29 #export MDB_VERSION="8.3.4-ent"
30 
31 # admin user connection string (for import and sharding commands)
32 #export MDB_ADMIN_CONNECTION_STRING="mongodb://mdb-admin:PASSWORD@HOST/?tls=true&tlsCAFile=/tls/ca-pem&authSource=admin&authMechanism=SCRAM-SHA-256"
33 
34 # regular user connection string (for search queries)
35 #export MDB_USER_CONNECTION_STRING="mongodb://mdb-user:PASSWORD@HOST/?tls=true&tlsCAFile=/tls/ca-pem&authSource=admin&authMechanism=SCRAM-SHA-256"
36 
37 # TLS CA ConfigMap mounted in the tools pod at /tls
38 #export MDB_TLS_CA_CONFIGMAP="ext-mdb-sh-ca"

To connect using the TLS-enabled connection strings defined in the env_variables.sh file, ensure that the CA certificate referenced by MDB_TLS_CA_CONFIGMAP is available so that the mongosh client can verify the server's TLS certificate.

Deploy a pod for MongoDB client tools.

Deploy a pod named mongodb-tools to run MongoDB client tools like mongosh and mongorestore from within the Kubernetes cluster. Running a pod inside the cluster simplifies connectivity to MongoDB without needing to expose the database externally.

The following command deploys a pod that runs a MongoDB server image, which ships with mongosh, and mounts the CA certificate from the MDB_TLS_CA_CONFIGMAP ConfigMap at /tls. The pod is kept running with a sleep infinity command so that you can use kubectl exec to run MongoDB client tools against your actual cluster.

1 echo "Deploying mongodb-tools pod..."
2 
3 TOOLS_IMAGE="mongodb/mongodb-community-server:${MDB_VERSION%-ent}-ubi8"
4 
5 echo "  Image: ${TOOLS_IMAGE}"
6 kubectl apply --context "${K8S_CTX}" -f - <<EOF
7 apiVersion: v1
8 kind: Pod
9 metadata:
10   name: mongodb-tools
11   namespace: ${MDB_NS}
12 spec:
13   restartPolicy: Never
14   containers:
15     - name: mongodb-tools
16       image: ${TOOLS_IMAGE}
17       command: ["sleep", "infinity"]
18       volumeMounts:
19         - name: tls
20           mountPath: /tls
21           readOnly: true
22   volumes:
23     - name: tls
24       configMap:
25         # CA certificate is mounted at /tls/ca-pem
26         name: ${MDB_TLS_CA_CONFIGMAP}
27 EOF
28 
29 # Wait for pod to be ready
30 echo "Waiting for mongodb-tools pod to be ready..."
31 kubectl wait --for=condition=Ready pod/mongodb-tools \
32   -n "${MDB_NS}" \
33   --context "${K8S_CTX}" \
34   --timeout=120s
35 
36 conn_str="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
37 echo "mongodb-tools pod is ready"
38 echo ""
39 echo "You can now run MongoDB commands using:"
40 echo "  kubectl exec -it mongodb-tools -n ${MDB_NS} -- mongosh \"${conn_str}\""

Import the sample data and shard the collection.

Import the sample_mflix database, which contains the following collections:

Collection Name	Description
`comments`	Contains comments associated with specific movies.
`embedded_movies`	Contains details on movies in the `Western`, `Action`, and `Fantasy` genres from the `movies` collection with the following additional fields: `plot_embedding_voyage_3_large` - Contains 2048d embeddings created from the `plot` field using Voyage AI's `voyage-3-large` embedding model and converted to `binData` for efficient storage and retrieval. `plot_embedding` - Contains 1536d embeddings created from the `plot` field using OpenAI's `text-embedding-ada-002` embedding model and converted to `binData` for efficient storage and retrieval.
`movies`	Contains movie information, including release year, director, and reviews.
`sessions`	Metadata field. Contains users' JSON Web Tokens.
`theaters`	Contains locations of movie theaters.
`users`	Contains user information.

The following command uses the admin connection string to:

Download the sample_mflix archive.
Load the collection using mongorestore.
Enable sharding on the sample_mflix database.
Create a hashed index on movies._id.
Shard the movies collection.

1 echo "Importing sample_mflix dataset..."
2 
3 admin_conn="${MDB_ADMIN_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
4 
5 echo "Downloading sample_mflix archive (~50MB)..."
6 kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
7   curl -sL 'https://atlas-education.s3.amazonaws.com/sample_mflix.archive' \
8     -o /tmp/sample_mflix.archive
9 echo "  Download complete"
10 
11 echo "Restoring database..."
12 kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
13   mongorestore --archive=/tmp/sample_mflix.archive \
14     --uri="${admin_conn}" \
15     --nsInclude='sample_mflix.*' \
16     --drop \
17     --numParallelCollections=1 \
18     --numInsertionWorkersPerCollection=1
19 echo "  Database restored"
20 
21 kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
22   rm -f /tmp/sample_mflix.archive
23 
24 echo "Configuring sharding..."
25 
26 kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
27   mongosh "${admin_conn}" --quiet --eval 'sh.enableSharding("sample_mflix")'
28 echo "  Enabled sharding on sample_mflix database"
29 
30 kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
31   mongosh "${admin_conn}" --quiet --eval \
32     'db.getSiblingDB("sample_mflix").movies.createIndex({ _id: "hashed" })'
33 echo "  Created hashed index on movies._id"
34 
35 kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
36   mongosh "${admin_conn}" --quiet --eval \
37     'sh.shardCollection("sample_mflix.movies", { _id: "hashed" })'
38 echo "  Sharded sample_mflix.movies collection"
39 
40 echo ""
41 echo "Sample data imported and sharded"

Create an index.

You can create a MongoDB Search or a Vector Search index. You must create a MongoDB Search index to run $search and $searchMeta queries, and Vector Search index to run $vectorSearch queries.

MongoDB Search provides flexible index definitions that support dynamic and static field mappings, various analyzers (standard, language-specific, custom), and features like synonyms and faceted search. To learn more, see Define Your Index.
Vector Search provides simple index definition that supports querying vector embeddings in your data based on semantic similarity by using ANN and ENN search algorithm. To learn more, see Define Vector Search Index.

Copy, paste, and run the following command in your terminal to create a MongoDB Search or a Vector Search index.

The command uses kubectl exec to run mongosh in the mongodb-tools pod using the user connection string and calls createSearchIndex() to create the index. Kubernetes Operator creates the indexes through mongos and automatically distributes them to each shard's mongot instance through the Envoy proxy, an L7 load balancer that the operator manages automatically.

The following command creates a MongoDB Search index named default with dynamic mappings on the movies collection. Dynamic mapping automatically indexes all dynamically indexable field types.

1 echo "Creating search index on sample_mflix.movies..."
2 
3 user_conn="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
4 
5 # shellcheck disable=SC2016
6 kubectl exec mongodb-tools \
7   -n "${MDB_NS}" \
8   --context "${K8S_CTX}" \
9   -- mongosh "${user_conn}" --quiet --eval '
10   const db_mflix = db.getSiblingDB("sample_mflix");
11 
12   // Check if index already exists
13   const result = db_mflix.runCommand({ listSearchIndexes: "movies" });
14   const existing = (result.ok && result.cursor && result.cursor.firstBatch) ? result.cursor.firstBatch : [];
15 
16   if (existing.some(idx => idx.name === "default")) {
17     print("Search index '\''default'\'' already exists");
18   } else {
19     // Create default text search index
20     db_mflix.movies.createSearchIndex({
21       name: "default",
22       definition: {
23         mappings: {
24           dynamic: true
25         }
26       }
27     });
28     print("Search index '\''default'\'' created");
29   }
30 
31   print("\nNote: Search index may take a few minutes to build and sync.");
32 '
33 
34 echo "Search index creation initiated"

1 echo "Creating vector search index" \
2   "on sample_mflix.embedded_movies..."
3 
4 user_conn="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
5 
6 # shellcheck disable=SC2016
7 kubectl exec mongodb-tools \
8   -n "${MDB_NS}" \
9   --context "${K8S_CTX}" \
10   -- mongosh "${user_conn}" --quiet --eval '
11   const db_mflix = db.getSiblingDB("sample_mflix");
12 
13   // Check if collection exists
14   const collections = db_mflix.getCollectionNames();
15   if (!collections.includes("embedded_movies")) {
16     print("Warning: embedded_movies collection not found.");
17     print("Vector search index creation skipped.");
18   } else {
19     // Check if index already exists
20     const result = db_mflix.runCommand({ listSearchIndexes: "embedded_movies" });
21     const existing = (result.ok && result.cursor && result.cursor.firstBatch) ? result.cursor.firstBatch : [];
22 
23     if (existing.some(idx => idx.name === "vector_index")) {
24       print("Vector search index '\''vector_index'\'' already exists");
25     } else {
26       // Create vector search index
27       db_mflix.embedded_movies.createSearchIndex({
28         name: "vector_index",
29         type: "vectorSearch",
30         definition: {
31           fields: [{
32             type: "vector",
33             path: "plot_embedding_voyage_3_large",
34             numDimensions: 2048,
35             similarity: "dotProduct",
36             quantization: "scalar"
37           }]
38         }
39       });
40       print("Vector search index '\''vector_index'\'' created");
41     }
42   }
43 
44   print("\nNote: Vector index may take a few minutes to build and sync.");
45 '
46 
47 echo "Vector search index creation initiated"

Verify that the index creation succeeded.

Run the following command to retrieve the list of indexes for the collection.

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"});'

Query the data using the index.

The following script defines the aggregation pipeline query criteria and uses the kubectl exec command to load mongosh to execute the query.

The following query searches the plot field for movie plots that contain the term drama adventure. It excludes movies in the comedy and romance genres.

1 echo "Executing text search query..."
2 echo ""
3 
4 user_conn="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
5 
6 kubectl exec -i mongodb-tools \
7   -n "${MDB_NS}" \
8   --context "${K8S_CTX}" \
9   -- mongosh --quiet "${user_conn}" <<'MONGOSH'
10 use sample_mflix;
11 db.movies.aggregate([
12   {
13     $search: {
14       "compound": {
15         "must": [ {
16           "text": {
17             "query": "baseball",
18             "path": "plot"
19           }
20         }],
21         "mustNot": [ {
22           "text": {
23             "query": ["Comedy", "Romance"],
24             "path": "genres"
25           }
26         } ]
27       },
28       "sort": {
29         "released": -1
30       }
31     }
32   },
33   {
34     $limit: 3
35   },
36   {
37     $project: {
38       "_id": 0,
39       "title": 1,
40       "plot": 1,
41       "genres": 1,
42       "released": 1
43     }
44   }
45 ]);
46 MONGOSH
47 
48 echo ""
49 echo "Search query executed successfully"

1 Executing text search query...
2 
3 Enterprise [direct: mongos] test> switched to db sample_mflix
4 Enterprise [direct: mongos] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
5   {
6     plot: 'A sports agent stages an unconventional recruitment strategy to get talented Indian cricket players to play Major League Baseball.',
7     genres: [ 'Biography', 'Drama', 'Sport' ],
8     title: 'Million Dollar Arm',
9     released: ISODate('2014-05-16T00:00:00.000Z')
10   },
11   {
12     plot: 'A Taiwanese high school baseball team travels to Japan in 1931 to compete in a national tournament.',
13     genres: [ 'Biography', 'Drama', 'History' ],
14     title: 'Kano',
15     released: ISODate('2014-02-27T00:00:00.000Z')
16   },
17   {
18     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 ...",
19     genres: [ 'Drama' ],
20     title: 'Calloused Hands',
21     released: ISODate('2013-03-03T00:00:00.000Z')
22   }
23 ]
24 Enterprise [direct: mongos] sample_mflix> 
25 Search query executed successfully

The following query searches the plot_embedding_voyage_3_large field using 2048 dimension vector embeddings for the string time travel, generated using Voyage AI's voyage-3-large model.

1 echo "Executing vector search query..."
2 echo ""
3 
4 user_conn="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
5 
6 kubectl exec -i mongodb-tools \
7   -n "${MDB_NS}" \
8   --context "${K8S_CTX}" \
9   -- mongosh --quiet "${user_conn}" <<'MONGOSH'
10 use sample_mflix;
11 
12 const sourceMovieTitle = "Back to the Future Part II";
13 const sourceMovie = db.embedded_movies.findOne(
14   {
15     title: sourceMovieTitle,
16     plot_embedding_voyage_3_large: { $exists: true }
17   },
18   { plot_embedding_voyage_3_large: 1 }
19 );
20 if (!sourceMovie) {
21   throw new Error(`No source movie named "${sourceMovieTitle}" contains plot_embedding_voyage_3_large`);
22 }
23 print(`Finding movies with plots similar to "${sourceMovieTitle}"`);
24 const queryVector = sourceMovie.plot_embedding_voyage_3_large;
25 
26 db.embedded_movies.aggregate([
27   {
28     $vectorSearch: {
29       index: "vector_index",
30       path: "plot_embedding_voyage_3_large",
31       queryVector,
32       numCandidates: 50,
33       limit: 6
34     }
35   },
36   { $match: { _id: { $ne: sourceMovie._id } } },
37   { $limit: 5 },
38   {
39     $project: {
40       _id: 0,
41       title: 1,
42       year: 1,
43       plot: 1,
44       score: { $meta: "vectorSearchScore" }
45     }
46   }
47 ]);
48 MONGOSH
49 
50 echo ""
51 echo "Vector search query executed successfully"

1 Executing vector search query...
2 
3 Enterprise [direct: mongos] test> switched to db sample_mflix
4 Enterprise [direct: mongos] sample_mflix> 
5 Enterprise [direct: mongos] sample_mflix> 
6 Enterprise [direct: mongos] sample_mflix> ... ... ... ... ... ... 
7 Enterprise [direct: mongos] sample_mflix> ... ... 
8 Enterprise [direct: mongos] sample_mflix> Finding movies with plots similar to "Back to the Future Part II"
9 
10 Enterprise [direct: mongos] sample_mflix> 
11 Enterprise [direct: mongos] sample_mflix> 
12 Enterprise [direct: mongos] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
13   {
14     year: 1990,
15     plot: 'Enjoying a peaceable existence in 1885, Doctor Emmet Brown is about to be killed by Buford "Mad Dog" Tannen. Marty McFly travels back in time to save his friend.',
16     title: 'Back to the Future Part III',
17     score: 0.9357404708862305
18   },
19   {
20     year: 2015,
21     plot: 'When John Connor, leader of the human resistance, sends Sgt. Kyle Reese back to 1984 to protect Sarah Connor and safeguard the future, an unexpected turn of events creates a fractured timeline.',
22     title: 'Terminator Genisys',
23     score: 0.9031341075897217
24   },
25   {
26     plot: 'A tyrant from the future creates evil android doubles of Bill and Ted and sends them back to eliminate the originals.',
27     title: "Bill & Ted's Bogus Journey",
28     year: 1991,
29     score: 0.9029397964477539
30   },
31   {
32     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...',
33     title: 'A.P.E.X.',
34     year: 1994,
35     score: 0.8977813720703125
36   },
37   {
38     plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
39     title: 'Love Story 2050',
40     year: 2008,
41     score: 0.897305965423584
42   }
43 ]
44 Enterprise [direct: mongos] sample_mflix> 
45 Vector search query executed successfully

Troubleshooting

Search Index Creation Fails

Symptoms: The createSearchIndex command times out or fails.

Check:

# Verify that the mongot pod is running
kubectl get pods -n ${MDB_NS} --context ${K8S_CTX} | grep search
# Check mongot logs for connection or sync errors
kubectl logs ${MDB_RESOURCE_NAME}-search-0-0 \
  -n ${MDB_NS} --context ${K8S_CTX} --tail=50
# Verify index status from the replica set
kubectl exec -n ${MDB_NS} --context ${K8S_CTX} \
  ${MDB_RESOURCE_NAME}-0 -c mongod -- \
  mongosh --quiet --eval \
  'db.getSiblingDB("sample_mflix").runCommand({ listSearchIndexes: "movies" })'

Common Causes:

The mongot pod is not ready.
The search-sync-source user credentials are incorrect or the password Secret was not created.

Verify by running the following command:
kubectl get secret ${MDB_RESOURCE_NAME}-search-sync-source-password -n ${MDB_NS}
TLS certificate mismatch between mongod and mongot.

The MongoDBSearch TLS certificate referenced by spec.security.tls.certificateKeySecretRef must be trusted by the MongoDBCommunity replica set members.

Deprecated since version 1.8.0: Use spec.security.tls.certsSecretPrefix instead.

Symptoms: The createSearchIndex command times out or fails.

Check:

# Verify that the mongot pod is running
kubectl get pods -n ${MDB_NS} --context ${K8S_CTX} | grep search
# Check mongot logs for connection or sync errors
kubectl logs ${MDB_RESOURCE_NAME}-search-0-0 \
  -n ${MDB_NS} --context ${K8S_CTX} --tail=50
# Verify index status from the replica set
kubectl exec -n ${MDB_NS} --context ${K8S_CTX} \
  ${MDB_RESOURCE_NAME}-0 -c mongodb-enterprise-database -- \
  mongosh --quiet --eval \
  'db.getSiblingDB("sample_mflix").runCommand({ listSearchIndexes: "movies" })'

Common Causes:

The mongot pod is not ready.
The search-sync-source user credentials are incorrect or the password Secret was not created.

Verify by running the following command:
kubectl get secret ${MDB_RESOURCE_NAME}-search-sync-source-password -n ${MDB_NS}
TLS certificate mismatch between mongod and mongot.

The MongoDBSearch TLS certificate referenced by spec.security.tls.certificateKeySecretRef must be trusted by the MongoDB replica set members.

Deprecated since version 1.8.0: Use spec.security.tls.certsSecretPrefix instead.

Symptoms: The createSearchIndex command times out or fails when run through mongos.

Check:

# Verify that all per-shard mongot pods are running
kubectl get pods -n ${MDB_NS} --context ${K8S_CTX} | grep search
# Check mongot logs for a specific shard
kubectl logs ${MDB_SEARCH_RESOURCE_NAME}-search-0-${MDB_EXTERNAL_SHARD_0_NAME}-0 \
  -n ${MDB_NS} --context ${K8S_CTX} --tail=50
# Verify index status from mongos
kubectl exec mongodb-tools -n ${MDB_NS} --context ${K8S_CTX} -- \
  mongosh "${MDB_USER_CONNECTION_STRING}" --quiet --eval \
  'db.getSiblingDB("sample_mflix").runCommand({ listSearchIndexes: "movies" })'

Common Causes:

mongot pods are not ready on one or more shards.

All per-shard mongot instances must be running before index creation succeeds.
The search-sync-source user credentials are incorrect or the password Secret was not created.

Verify by running the following command:
kubectl get secret ${MDB_SEARCH_RESOURCE_NAME}-search-sync-source-password -n ${MDB_NS}
TLS CA mismatch between mongod and mongot.

The CA certificate in MDB_TLS_CA_CONFIGMAP must be the same CA that signed the external mongod server certificates.
The Envoy proxy is not routing traffic correctly.

Verify that per-shard proxy Services exist:
kubectl get svc -n ${MDB_NS} | grep proxy-svc
The mongotHost parameter on the external mongod instances does not match the Envoy proxy Service endpoint for that shard.

`MongoDBSearch` Stuck in Pending

Symptoms: The MongoDBSearch resource stays in Pending state and never transitions to Running.

Check:

kubectl describe mongodbsearch ${MDB_RESOURCE_NAME} \
  -n ${MDB_NS} --context ${K8S_CTX}
kubectl get events -n ${MDB_NS} --context ${K8S_CTX} \
  --field-selector involvedObject.name=${MDB_RESOURCE_NAME}
# Check operator logs for reconciliation errors
kubectl logs -l app.kubernetes.io/name=mongodb-kubernetes-operator \
  -n ${MDB_NS} --context ${K8S_CTX} --tail=100

Common Causes:

The password Secret for the search-sync-source user was not created. The MongoDBSearch CR expects a Secret named ${MDB_RESOURCE_NAME}-search-sync-source-password.
TLS certificate Secrets are missing. Verify with kubectl get secrets -n ${MDB_NS} | grep tls.
The MongoDBCommunity replica set is not in Running state. The operator waits for the source resource before deploying the mongot pod.
The mongot container image cannot be pulled. Check for image pull errors with kubectl get events -n ${MDB_NS} | grep pull.

Symptoms: The MongoDBSearch resource stays in Pending state and never transitions to Running.

Check:

kubectl describe mongodbsearch ${MDB_RESOURCE_NAME} \
  -n ${MDB_NS} --context ${K8S_CTX}
kubectl get events -n ${MDB_NS} --context ${K8S_CTX} \
  --field-selector involvedObject.name=${MDB_RESOURCE_NAME}
# Check operator logs for reconciliation errors
kubectl logs -l app.kubernetes.io/name=enterprise-operator \
  -n ${MDB_NS} --context ${K8S_CTX} --tail=100

Common Causes:

The password Secret for the search-sync-source user was not created. The MongoDBSearch CR expects a Secret named ${MDB_RESOURCE_NAME}-search-sync-source-password.
TLS certificate Secrets are missing. Verify with kubectl get secrets -n ${MDB_NS} | grep tls.
The MongoDB replica set is not in Running state. The operator waits for the source MongoDB resource before deploying the mongot pod.
The mongot container image cannot be pulled. Check for image pull errors with kubectl get events -n ${MDB_NS} | grep pull.

Symptoms: The MongoDBSearch resource stays in Pending state and never transitions to Running.

Check:

kubectl describe mongodbsearch ${MDB_SEARCH_RESOURCE_NAME} \
  -n ${MDB_NS} --context ${K8S_CTX}
kubectl get events -n ${MDB_NS} --context ${K8S_CTX} \
  --field-selector involvedObject.name=${MDB_SEARCH_RESOURCE_NAME}
# Check operator logs for reconciliation errors
kubectl logs -l app.kubernetes.io/name=enterprise-operator \
  -n ${MDB_NS} --context ${K8S_CTX} --tail=100

Common Causes:

The password Secret for the search-sync-source user was not created. The MongoDBSearch CR expects a Secret named ${MDB_SEARCH_RESOURCE_NAME}-search-sync-source-password.
TLS certificate Secrets are missing. Verify with kubectl get secrets -n ${MDB_NS} | grep tls.
The external sharded cluster hostnames in spec.source.external.shardedCluster.router.hosts and spec.source.external.shardedCluster.shards[].hosts are incorrect or unreachable from the Kubernetes cluster.
The mongot container image cannot be pulled. Check for image pull errors with kubectl get events -n ${MDB_NS} | grep pull.

Search Index Management Service Connection Error

This issue applies to deployments that use a managed Envoy load balancer (spec.loadBalancer.managed).

Symptoms: When you create a MongoDB Search or Vector Search index, the command fails with the following error:

MongoServerError[CommandFailed]: Error connecting to Search Index Management service.

Check:

# Inspect the DNS names in the load balancer server certificate
kubectl get secret \
  ${MDB_TLS_CERT_SECRET_PREFIX}-${MDB_RESOURCE_NAME}-search-lb-0-cert \
  -n ${MDB_NS} --context ${K8S_CTX} \
  -o "jsonpath={.data['tls\.crt']}" | base64 -d | \
  openssl x509 -noout -text | grep -A1 "Subject Alternative Name"
# Inspect the DNS names in the load balancer client certificate
kubectl get secret \
  ${MDB_TLS_CERT_SECRET_PREFIX}-${MDB_RESOURCE_NAME}-search-lb-0-client-cert \
  -n ${MDB_NS} --context ${K8S_CTX} \
  -o "jsonpath={.data['tls\.crt']}" | base64 -d | \
  openssl x509 -noout -text | grep -A1 "Subject Alternative Name"

Common Causes:

The DNS names in the load balancer client or server certificates don't include the load balancer hostname that mongod uses to connect to mongot.

Review the DNS names in the certificates. If they aren't correct, update the certificates and re-create the certificate Secrets. For the load balancer certificate naming conventions, see Deploy MongoDB Search and Vector Search.

Symptoms: When you create a MongoDB Search or Vector Search index through mongos, the command fails with the following error:

MongoServerError[CommandFailed]: Error connecting to Search Index Management service.

Check:

# Inspect the DNS names in the load balancer server certificate
kubectl get secret \
  ${MDB_TLS_CERT_SECRET_PREFIX}-${MDB_SEARCH_RESOURCE_NAME}-search-lb-0-cert \
  -n ${MDB_NS} --context ${K8S_CTX} \
  -o "jsonpath={.data['tls\.crt']}" | base64 -d | \
  openssl x509 -noout -text | grep -A1 "Subject Alternative Name"
# Inspect the DNS names in the load balancer client certificate
kubectl get secret \
  ${MDB_TLS_CERT_SECRET_PREFIX}-${MDB_SEARCH_RESOURCE_NAME}-search-lb-0-client-cert \
  -n ${MDB_NS} --context ${K8S_CTX} \
  -o "jsonpath={.data['tls\.crt']}" | base64 -d | \
  openssl x509 -noout -text | grep -A1 "Subject Alternative Name"

Common Causes:

The wildcard SAN in the load balancer server certificate doesn't cover all per-shard proxy Service host names that mongod uses to connect to mongot through SNI routing.
The load balancer client certificate is not signed by the CA that mongot trusts.

Review the SANs in the server certificate and verify that the client certificate is signed by the correct CA. If they aren't correct, update the certificates and re-create the certificate Secrets. For the load balancer certificate naming conventions, see Deploy MongoDB Search and Vector Search.

Resources

To learn more about creating indexes and running search queries, see:

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	# TLS-related resources used by the snippets in this module
19	#export MDB_TLS_CA_SECRET_NAME="${MDB_RESOURCE_NAME}-ca"
20	#export MDB_TLS_CA_CONFIGMAP="${MDB_RESOURCE_NAME}-ca-configmap"
21	#export MDB_TLS_SERVER_CERT_SECRET_NAME="${MDB_RESOURCE_NAME}-tls"
22	#export MDB_SEARCH_TLS_SECRET_NAME="${MDB_RESOURCE_NAME}-search-tls"
23
24	# default connection string if MongoDB database is deployed using the operator
25	#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=${MDB_RESOURCE_NAME}"
26	# To enable TLS for the shared snippets, append driver options directly to the connection string, for example:
27	#export MDB_CONNECTION_STRING="mongodb://mdb-user:${MDB_USER_PASSWORD}@${MDB_RESOURCE_NAME}-0.${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local:27017/?replicaSet=${MDB_RESOURCE_NAME}&tls=true&tlsCAFile=/tls/ca.crt"

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%-ent}-ubi8
12	command: ["/bin/bash", "-c"]
13	args: ["sleep infinity"]
14	volumeMounts:
15	- name: mongo-ca
16	mountPath: /tls
17	readOnly: true
18	restartPolicy: Never
19	volumes:
20	- name: mongo-ca
21	configMap:
22	name: ${MDB_TLS_CA_CONFIGMAP:-"${MDB_RESOURCE_NAME}-ca-configmap"}
23	optional: true
24	items:
25	- key: ca.crt
26	path: ca.crt
27	EOF
28
29	echo "Waiting for the mongodb-tools to be ready..."
30	kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait --for=condition=Ready pod/mongodb-tools-pod --timeout=60s

1	echo "Downloading sample database archive..."
2	kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" mongodb-tools-pod -- \
3	curl -fSL https://atlas-education.s3.amazonaws.com/sample_mflix.archive \
4	-o /tmp/sample_mflix.archive
5
6	echo "Restoring sample database..."
7	kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" mongodb-tools-pod -- \
8	mongorestore \
9	--archive=/tmp/sample_mflix.archive \
10	--verbose=1 \
11	--drop \
12	--nsInclude 'sample_mflix.*' \
13	--uri="${MDB_CONNECTION_STRING}"

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.movies.createSearchIndex("default", { mappings: { dynamic: true } });'

1	{
2	cursor: {
3	id: 0,
4	ns: 'sample_mflix.movies',
5	firstBatch: [
6	{
7	id: '6abb9dd880557b2f2227865f',
8	name: 'default',
9	status: 'READY',
10	queryable: true,
11	latestDefinitionVersion: { version: 0, createdAt: ISODate('2026-09-29T11:15:36.000Z') },
12	latestDefinition: { mappings: { dynamic: true } },
13	statusDetail: [
14	{
15	hostname: 'mdbc-rs-search-0-0.6abb9ca688d67b6ecf5a64f6',
16	status: 'READY',
17	queryable: true,
18	mainIndex: {
19	status: 'READY',
20	queryable: true,
21	definitionVersion: {
22	version: 0,
23	createdAt: ISODate('2026-09-29T11:15:36.000Z')
24	},
25	definition: { mappings: { dynamic: true, fields: {} } }
26	}
27	}
28	]
29	},
30	{
31	id: '6abb9dd980557b2f22278661',
32	name: 'vector_auto_embed_index',
33	status: 'READY',
34	queryable: true,
35	latestDefinitionVersion: { version: 0, createdAt: ISODate('2026-09-29T11:15:37.000Z') },
36	latestDefinition: {
37	fields: [
38	{
39	type: 'autoEmbed',
40	modality: 'text',
41	path: 'plot',
42	model: 'voyage-4'
43	}
44	]
45	},
46	statusDetail: [
47	{
48	hostname: 'mdbc-rs-search-0-0.6abb9ca688d67b6ecf5a64f6',
49	status: 'READY',
50	queryable: true,
51	mainIndex: {
52	status: 'READY',
53	queryable: true,
54	definitionVersion: {
55	version: 0,
56	createdAt: ISODate('2026-09-29T11:15:37.000Z')
57	},
58	definition: {
59	fields: [
60	{
61	type: 'autoEmbed',
62	path: 'plot',
63	numDimensions: 1024,
64	similarity: 'dotProduct',
65	quantization: 'float',
66	model: 'voyage-4',
67	modality: 'text'
68	}
69	]
70	}
71	}
72	}
73	]
74	}
75	]
76	},
77	ok: 1,
78	'$clusterTime': {
79	clusterTime: Timestamp({ t: 1790680626, i: 1 }),
80	signature: {
81	hash: Binary.createFromBase64('4x00PUrbiAWIbepjvexGKb/uA9Q=', 0),
82	keyId: Long('7690912939544412168')
83	}
84	},
85	operationTime: Timestamp({ t: 1790680626, i: 1 })
86	}

1	kubectl exec -i --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
2	mongosh --quiet "${MDB_CONNECTION_STRING}" <<'EOF'
3	use sample_mflix;
4	db.movies.aggregate([
5	{
6	$search: {
7	"compound": {
8	"must": [ {
9	"text": {
10	"query": "baseball",
11	"path": "plot"
12	}
13	}],
14	"mustNot": [ {
15	"text": {
16	"query": ["Comedy", "Romance"],
17	"path": "genres"
18	}
19	} ]
20	},
21	"sort": {
22	"released": -1
23	}
24	}
25	},
26	{
27	$limit: 3
28	},
29	{
30	$project: {
31	"_id": 0,
32	"title": 1,
33	"plot": 1,
34	"genres": 1,
35	"released": 1
36	}
37	}
38	]);
39	EOF

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>

1	mdbc-rs [primary] test> switched to db sample_mflix
2	mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
3	{
4	plot: 'A highly intellectual but socially awkward spy is tasked with preventing a terrorist attack from a Russian spy agency.',
5	title: 'Get Smart',
6	score: 0.8280618190765381
7	},
8	{
9	plot: 'A dramatization of the missions and adventures of the greatest spy in British history.',
10	title: 'Reilly: Ace of Spies',
11	score: 0.8077303767204285
12	},
13	{
14	plot: 'An American agent, under false suspicion of disloyalty, must discover and expose the real spy without the help of his organization.',
15	title: 'Mission: Impossible',
16	score: 0.8074153661727905
17	},
18	{
19	plot: 'A researcher for the CIA who convinces his superiors to send him to the eastern bloc in order to avenge the murder of his wife by enemy agents discovers a web of deception underneath his ...',
20	title: 'The Amateur',
21	score: 0.8071699738502502
22	},
23	{
24	plot: 'A French intelligence agent becomes embroiled in the Cold War politics first with uncovering the events leading up to the 1962 Cuban Missle Crisis, and then back to France to break up an international Russian spy ring.',
25	title: 'Topaz',
26	score: 0.8057430982589722
27	},
28	{
29	plot: 'A bookish CIA researcher finds all his co-workers dead, and must outwit those responsible until he figures out who he can really trust.',
30	title: 'Three Days of the Condor',
31	score: 0.8037759065628052
32	},
33	{
34	plot: 'An unsuspecting, disenchanted man finds himself working as a spy in the dangerous, high-stakes world of corporate espionage. Quickly getting way over-his-head, he teams up with a mysterious femme fatale.',
35	title: 'Cypher',
36	score: 0.8033789396286011
37	},
38	{
39	plot: 'Two ex-government agents turned rival industrial spies have to be at the top of their game when one of their companies prepares to launch a major product. However, they distract each other in more ways than one.',
40	title: 'Duplicity',
41	score: 0.8026910424232483
42	},
43	{
44	plot: 'A CIA agent goes on the run after a defector accuses her of being a Russian spy.',
45	title: 'Salt',
46	score: 0.8025847673416138
47	},
48	{
49	plot: 'In the bleak days of the Cold War, espionage veteran George Smiley is forced from semi-retirement to uncover a Soviet agent within MI6.',
50	title: 'Tinker Tailor Soldier Spy',
51	score: 0.8008040189743042
52	}
53	]
54	mdbc-rs [primary] sample_mflix>

1	# The env vars here are all commented out because this
2	# snippets module is reusable across different sharded
3	# MongoDB Search deployments and all the necessary
4	# variables should be already defined there.
5	#
6	# Required env var contract:
7	# MDB_ADMIN_CONNECTION_STRING
8	# — for import/sharding commands (admin privileges)
9	# MDB_USER_CONNECTION_STRING
10	# — for search queries (regular user privileges)
11	# MDB_CONNECTION_STRING
12	# — fallback if the above are not set
13	# (e.g., non-sharded usage)
14	# K8S_CTX — Kubernetes context
15	# MDB_NS — Kubernetes namespace
16	# MDB_VERSION
17	# — MongoDB version (e.g., 8.2.6-ent)
18	# MDB_TLS_CA_CONFIGMAP
19	# — CA certificate ConfigMap name for tools pod
20	# TLS mount
21
22	# set it to the context name of the k8s cluster
23	#export K8S_CTX="<local cluster context>"
24	# the following namespace will be used to deploy
25	# mongodb tools pod
26	#export MDB_NS="mongodb"
27
28	# MongoDB version (used for tools pod image)
29	#export MDB_VERSION="8.3.4-ent"
30
31	# admin user connection string (for import and sharding commands)
32	#export MDB_ADMIN_CONNECTION_STRING="mongodb://mdb-admin:PASSWORD@HOST/?tls=true&tlsCAFile=/tls/ca-pem&authSource=admin&authMechanism=SCRAM-SHA-256"
33
34	# regular user connection string (for search queries)
35	#export MDB_USER_CONNECTION_STRING="mongodb://mdb-user:PASSWORD@HOST/?tls=true&tlsCAFile=/tls/ca-pem&authSource=admin&authMechanism=SCRAM-SHA-256"
36
37	# TLS CA ConfigMap mounted in the tools pod at /tls
38	#export MDB_TLS_CA_CONFIGMAP="ext-mdb-sh-ca"

1	echo "Deploying mongodb-tools pod..."
2
3	TOOLS_IMAGE="mongodb/mongodb-community-server:${MDB_VERSION%-ent}-ubi8"
4
5	echo " Image: ${TOOLS_IMAGE}"
6	kubectl apply --context "${K8S_CTX}" -f - <<EOF
7	apiVersion: v1
8	kind: Pod
9	metadata:
10	name: mongodb-tools
11	namespace: ${MDB_NS}
12	spec:
13	restartPolicy: Never
14	containers:
15	- name: mongodb-tools
16	image: ${TOOLS_IMAGE}
17	command: ["sleep", "infinity"]
18	volumeMounts:
19	- name: tls
20	mountPath: /tls
21	readOnly: true
22	volumes:
23	- name: tls
24	configMap:
25	# CA certificate is mounted at /tls/ca-pem
26	name: ${MDB_TLS_CA_CONFIGMAP}
27	EOF
28
29	# Wait for pod to be ready
30	echo "Waiting for mongodb-tools pod to be ready..."
31	kubectl wait --for=condition=Ready pod/mongodb-tools \
32	-n "${MDB_NS}" \
33	--context "${K8S_CTX}" \
34	--timeout=120s
35
36	conn_str="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
37	echo "mongodb-tools pod is ready"
38	echo ""
39	echo "You can now run MongoDB commands using:"
40	echo " kubectl exec -it mongodb-tools -n ${MDB_NS} -- mongosh \"${conn_str}\""

1	echo "Importing sample_mflix dataset..."
2
3	admin_conn="${MDB_ADMIN_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
4
5	echo "Downloading sample_mflix archive (~50MB)..."
6	kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
7	curl -sL 'https://atlas-education.s3.amazonaws.com/sample_mflix.archive' \
8	-o /tmp/sample_mflix.archive
9	echo " Download complete"
10
11	echo "Restoring database..."
12	kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
13	mongorestore --archive=/tmp/sample_mflix.archive \
14	--uri="${admin_conn}" \
15	--nsInclude='sample_mflix.*' \
16	--drop \
17	--numParallelCollections=1 \
18	--numInsertionWorkersPerCollection=1
19	echo " Database restored"
20
21	kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
22	rm -f /tmp/sample_mflix.archive
23
24	echo "Configuring sharding..."
25
26	kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
27	mongosh "${admin_conn}" --quiet --eval 'sh.enableSharding("sample_mflix")'
28	echo " Enabled sharding on sample_mflix database"
29
30	kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
31	mongosh "${admin_conn}" --quiet --eval \
32	'db.getSiblingDB("sample_mflix").movies.createIndex({ _id: "hashed" })'
33	echo " Created hashed index on movies._id"
34
35	kubectl exec mongodb-tools -n "${MDB_NS}" --context "${K8S_CTX}" -- \
36	mongosh "${admin_conn}" --quiet --eval \
37	'sh.shardCollection("sample_mflix.movies", { _id: "hashed" })'
38	echo " Sharded sample_mflix.movies collection"
39
40	echo ""
41	echo "Sample data imported and sharded"

1	echo "Creating search index on sample_mflix.movies..."
2
3	user_conn="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
4
5	# shellcheck disable=SC2016
6	kubectl exec mongodb-tools \
7	-n "${MDB_NS}" \
8	--context "${K8S_CTX}" \
9	-- mongosh "${user_conn}" --quiet --eval '
10	const db_mflix = db.getSiblingDB("sample_mflix");
11
12	// Check if index already exists
13	const result = db_mflix.runCommand({ listSearchIndexes: "movies" });
14	const existing = (result.ok && result.cursor && result.cursor.firstBatch) ? result.cursor.firstBatch : [];
15
16	if (existing.some(idx => idx.name === "default")) {
17	print("Search index '\''default'\'' already exists");
18	} else {
19	// Create default text search index
20	db_mflix.movies.createSearchIndex({
21	name: "default",
22	definition: {
23	mappings: {
24	dynamic: true
25	}
26	}
27	});
28	print("Search index '\''default'\'' created");
29	}
30
31	print("\nNote: Search index may take a few minutes to build and sync.");
32	'
33
34	echo "Search index creation initiated"

1	echo "Creating vector search index" \
2	"on sample_mflix.embedded_movies..."
3
4	user_conn="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
5
6	# shellcheck disable=SC2016
7	kubectl exec mongodb-tools \
8	-n "${MDB_NS}" \
9	--context "${K8S_CTX}" \
10	-- mongosh "${user_conn}" --quiet --eval '
11	const db_mflix = db.getSiblingDB("sample_mflix");
12
13	// Check if collection exists
14	const collections = db_mflix.getCollectionNames();
15	if (!collections.includes("embedded_movies")) {
16	print("Warning: embedded_movies collection not found.");
17	print("Vector search index creation skipped.");
18	} else {
19	// Check if index already exists
20	const result = db_mflix.runCommand({ listSearchIndexes: "embedded_movies" });
21	const existing = (result.ok && result.cursor && result.cursor.firstBatch) ? result.cursor.firstBatch : [];
22
23	if (existing.some(idx => idx.name === "vector_index")) {
24	print("Vector search index '\''vector_index'\'' already exists");
25	} else {
26	// Create vector search index
27	db_mflix.embedded_movies.createSearchIndex({
28	name: "vector_index",
29	type: "vectorSearch",
30	definition: {
31	fields: [{
32	type: "vector",
33	path: "plot_embedding_voyage_3_large",
34	numDimensions: 2048,
35	similarity: "dotProduct",
36	quantization: "scalar"
37	}]
38	}
39	});
40	print("Vector search index '\''vector_index'\'' created");
41	}
42	}
43
44	print("\nNote: Vector index may take a few minutes to build and sync.");
45	'
46
47	echo "Vector search index creation initiated"

1	echo "Executing text search query..."
2	echo ""
3
4	user_conn="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
5
6	kubectl exec -i mongodb-tools \
7	-n "${MDB_NS}" \
8	--context "${K8S_CTX}" \
9	-- mongosh --quiet "${user_conn}" <<'MONGOSH'
10	use sample_mflix;
11	db.movies.aggregate([
12	{
13	$search: {
14	"compound": {
15	"must": [ {
16	"text": {
17	"query": "baseball",
18	"path": "plot"
19	}
20	}],
21	"mustNot": [ {
22	"text": {
23	"query": ["Comedy", "Romance"],
24	"path": "genres"
25	}
26	} ]
27	},
28	"sort": {
29	"released": -1
30	}
31	}
32	},
33	{
34	$limit: 3
35	},
36	{
37	$project: {
38	"_id": 0,
39	"title": 1,
40	"plot": 1,
41	"genres": 1,
42	"released": 1
43	}
44	}
45	]);
46	MONGOSH
47
48	echo ""
49	echo "Search query executed successfully"

1	Executing text search query...
2
3	Enterprise [direct: mongos] test> switched to db sample_mflix
4	Enterprise [direct: mongos] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
5	{
6	plot: 'A sports agent stages an unconventional recruitment strategy to get talented Indian cricket players to play Major League Baseball.',
7	genres: [ 'Biography', 'Drama', 'Sport' ],
8	title: 'Million Dollar Arm',
9	released: ISODate('2014-05-16T00:00:00.000Z')
10	},
11	{
12	plot: 'A Taiwanese high school baseball team travels to Japan in 1931 to compete in a national tournament.',
13	genres: [ 'Biography', 'Drama', 'History' ],
14	title: 'Kano',
15	released: ISODate('2014-02-27T00:00:00.000Z')
16	},
17	{
18	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 ...",
19	genres: [ 'Drama' ],
20	title: 'Calloused Hands',
21	released: ISODate('2013-03-03T00:00:00.000Z')
22	}
23	]
24	Enterprise [direct: mongos] sample_mflix>
25	Search query executed successfully

1	echo "Executing vector search query..."
2	echo ""
3
4	user_conn="${MDB_USER_CONNECTION_STRING:-${MDB_CONNECTION_STRING}}"
5
6	kubectl exec -i mongodb-tools \
7	-n "${MDB_NS}" \
8	--context "${K8S_CTX}" \
9	-- mongosh --quiet "${user_conn}" <<'MONGOSH'
10	use sample_mflix;
11
12	const sourceMovieTitle = "Back to the Future Part II";
13	const sourceMovie = db.embedded_movies.findOne(
14	{
15	title: sourceMovieTitle,
16	plot_embedding_voyage_3_large: { $exists: true }
17	},
18	{ plot_embedding_voyage_3_large: 1 }
19	);
20	if (!sourceMovie) {
21	throw new Error(`No source movie named "${sourceMovieTitle}" contains plot_embedding_voyage_3_large`);
22	}
23	print(`Finding movies with plots similar to "${sourceMovieTitle}"`);
24	const queryVector = sourceMovie.plot_embedding_voyage_3_large;
25
26	db.embedded_movies.aggregate([
27	{
28	$vectorSearch: {
29	index: "vector_index",
30	path: "plot_embedding_voyage_3_large",
31	queryVector,
32	numCandidates: 50,
33	limit: 6
34	}
35	},
36	{ $match: { _id: { $ne: sourceMovie._id } } },
37	{ $limit: 5 },
38	{
39	$project: {
40	_id: 0,
41	title: 1,
42	year: 1,
43	plot: 1,
44	score: { $meta: "vectorSearchScore" }
45	}
46	}
47	]);
48	MONGOSH
49
50	echo ""
51	echo "Vector search query executed successfully"

1	Executing vector search query...
2
3	Enterprise [direct: mongos] test> switched to db sample_mflix
4	Enterprise [direct: mongos] sample_mflix>
5	Enterprise [direct: mongos] sample_mflix>
6	Enterprise [direct: mongos] sample_mflix> ... ... ... ... ... ...
7	Enterprise [direct: mongos] sample_mflix> ... ...
8	Enterprise [direct: mongos] sample_mflix> Finding movies with plots similar to "Back to the Future Part II"
9
10	Enterprise [direct: mongos] sample_mflix>
11	Enterprise [direct: mongos] sample_mflix>
12	Enterprise [direct: mongos] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
13	{
14	year: 1990,
15	plot: 'Enjoying a peaceable existence in 1885, Doctor Emmet Brown is about to be killed by Buford "Mad Dog" Tannen. Marty McFly travels back in time to save his friend.',
16	title: 'Back to the Future Part III',
17	score: 0.9357404708862305
18	},
19	{
20	year: 2015,
21	plot: 'When John Connor, leader of the human resistance, sends Sgt. Kyle Reese back to 1984 to protect Sarah Connor and safeguard the future, an unexpected turn of events creates a fractured timeline.',
22	title: 'Terminator Genisys',
23	score: 0.9031341075897217
24	},
25	{
26	plot: 'A tyrant from the future creates evil android doubles of Bill and Ted and sends them back to eliminate the originals.',
27	title: "Bill & Ted's Bogus Journey",
28	year: 1991,
29	score: 0.9029397964477539
30	},
31	{
32	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...',
33	title: 'A.P.E.X.',
34	year: 1994,
35	score: 0.8977813720703125
36	},
37	{
38	plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
39	title: 'Love Story 2050',
40	year: 2008,
41	score: 0.897305965423584
42	}
43	]
44	Enterprise [direct: mongos] sample_mflix>
45	Vector search query executed successfully

Use MongoDB Search and Vector Search

Prerequisites

Procedure

Set the environment variables.

Deploy a pod for MongoDB client tools.

Import the sample data.

Create an index.

Create Search Index

Vector Search Index

Automated Embedding Index

Search Index

Vector Search Index

Automated Embedding Index

Query the data using the index.

Full-Text Search Query

Vector Search Query

Automated Embedding Query

Set the environment variables.

Deploy a pod for MongoDB client tools.

Import the sample data.

Create an index.

Create Search Index

Vector Search Index

Automated Embedding Index

Search Index

Vector Search Index

Automated Embedding Index

Query the data using the index.

Full-Text Search Query

Vector Search Query

Automated Embedding Query

Set the environment variables.

Deploy a pod for MongoDB client tools.

Import the sample data and shard the collection.

Create an index.

Search Index

Vector Search Index

Search Index

Vector Search Index

Query the data using the index.

Search Query

Vector Search Query

Troubleshooting

Search Index Creation Fails

MongoDBSearch Stuck in Pending

Search Index Management Service Connection Error

Resources

`MongoDBSearch` Stuck in Pending