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Deploy Search & Vector Search

Use MongoDB Search and Vector Search

If you installed MongoDBSearch resource successfully, 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 data, and run MongoDB Search and Vector Search queries against the data.

Prerequisites

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

  • Successful deployment of MongoDB and MongoDBSearch.

    To learn more, see:

    • Install and Use Search With MongoDB Enterprise Edition

    • Install and Use MongoDB Search and Vector Search With External MongoDB Enterprise Edition

  • User with permissions to create MongoDB Search and Vector Search and run MongoDB Search and Vector Search queries.

    To learn more, see Configure a Cloud Manager or Ops Manager User for MongoDB Search and Vector Search.

  • Kubernetes command-line tool, kubectl, configured to communicate with your cluster.

  • Bash v5.1 or higher for running the commands in this tutorial.

Procedure

1

Set the environment variables.

  1. 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"

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

2

Deploy a MongoDB tools pod.

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 neeeding to expose the database externally.

The following command deploys a pod named mongodb-tools-pod that runs a MongoDB Community Server image. The pod is kept running with a sleep infinity command to allow you to use kubectl exec to run MongoDB client tools.

1kubectl apply -n "${MDB_NS}" --context "${K8S_CTX}" -f - <<EOF
2apiVersion: v1
3kind: Pod
4metadata:
5 name: mongodb-tools-pod
6 labels:
7 app: mongodb-tools
8spec:
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
27EOF
28
29echo "Waiting for the mongodb-tools to be ready..."
30kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait --for=condition=Ready pod/mongodb-tools-pod --timeout=60s
3

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# Restore sample_mflix database. Provide any TLS parameters directly within MDB_CONNECTION_STRING.
2kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" \
3 mongodb-tools-pod -- env MDB_CONNECTION_STRING="${MDB_CONNECTION_STRING}" /bin/bash -eu -c "$(cat <<'EOF'
4echo "Downloading sample database archive..."
5curl -fSL https://atlas-education.s3.amazonaws.com/sample_mflix.archive -o /tmp/sample_mflix.archive
6
7echo "Restoring sample database"
8mongorestore \
9 --archive=/tmp/sample_mflix.archive \
10 --verbose=1 \
11 --drop \
12 --nsInclude 'sample_mflix.*' \
13 --uri="${MDB_CONNECTION_STRING}"
14EOF
15)"
4

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.

  1. 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 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.

    1kubectl 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 number of dimensions in the embedded_movies collection for running ANN queries.

    1kubectl 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 } ] });'

  2. Wait for index creation to complete.

    Index creation is an asynchronous process. Run the following script to wait for 60 seconds for the index creation to complete.

    1# Currently it's not possible to check the status of search indexes, we need to just wait
    2echo "Sleeping to wait for search indexes to be created"
    3sleep 60

    Alternatively, run the following command to retrieve the list of indexes for the collection.

    1kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
    2 mongosh --quiet "${MDB_CONNECTION_STRING}" \
    3 --eval "use sample_mflix" \
    4 --eval 'db.runCommand({"listSearchIndexes": "movies"});'
    1kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
    2 mongosh --quiet "${MDB_CONNECTION_STRING}" \
    3 --eval "use sample_mflix" \
    4 --eval 'db.runCommand({"listSearchIndexes": "embedded_movies"});'
5

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 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.

1mdb_script=$(cat <<'EOF'
2use sample_mflix;
3db.movies.aggregate([
4 {
5 $search: {
6 "compound": {
7 "must": [ {
8 "text": {
9 "query": "baseball",
10 "path": "plot"
11 }
12 }],
13 "mustNot": [ {
14 "text": {
15 "query": ["Comedy", "Romance"],
16 "path": "genres"
17 }
18 } ]
19 },
20 "sort": {
21 "released": -1
22 }
23 }
24 },
25 {
26 $limit: 3
27 },
28 {
29 $project: {
30 "_id": 0,
31 "title": 1,
32 "plot": 1,
33 "genres": 1,
34 "released": 1
35 }
36 }
37]);
38EOF
39)
40
41kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" \
42 mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF
43echo '${mdb_script}' > /tmp/mdb_script.js
44mongosh --quiet "${MDB_CONNECTION_STRING}" < /tmp/mdb_script.js
45EOF
46)"
1mdbc-rs [primary] test> switched to db sample_mflix
2mdbc-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]
22mdbc-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.

1mdb_script=$(cat <<'EOF'
2use sample_mflix;
3db.embedded_movies.aggregate([
4 {
5 "$vectorSearch": {
6 "index": "vector_index",
7 "path": "plot_embedding_voyage_3_large",
8 "queryVector": 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9 "numCandidates": 150,
10 "limit": 10,
11 "quantization": "scalar"
12 }
13 },
14 {
15 "$project": {
16 "_id": 0,
17 "plot": 1,
18 "title": 1,
19 "score": { $meta: "vectorSearchScore" }
20 }
21 }
22]);
23EOF
24)
25
26kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF
27echo '${mdb_script}' > /tmp/mdb_script.js
28mongosh --quiet "${MDB_CONNECTION_STRING}" < /tmp/mdb_script.js
29EOF
30)"
1mdbc-rs [primary] test> switched to db sample_mflix
2mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
3 {
4 plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
5 title: 'About Time',
6 score: 0.7704131603240967
7 },
8 {
9 plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
10 title: 'Retroactive',
11 score: 0.7597770690917969
12 },
13 {
14 plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
15 title: 'Timecop',
16 score: 0.7574796676635742
17 },
18 {
19 plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
20 title: 'A.P.E.X.',
21 score: 0.7573235034942627
22 },
23 {
24 plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.',
25 title: 'Back to the Future Part II',
26 score: 0.751945972442627
27 },
28 {
29 plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
30 title: 'Thrill Seekers',
31 score: 0.7503504753112793
32 },
33 {
34 plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
35 title: 'The Time Machine',
36 score: 0.750007152557373
37 },
38 {
39 plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...',
40 title: 'Timerider: The Adventure of Lyle Swann',
41 score: 0.7499568462371826
42 },
43 {
44 plot: 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.',
45 title: "The Time Traveler's Wife",
46 score: 0.7492842674255371
47 },
48 {
49 plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.',
50 title: 'The Final Countdown',
51 score: 0.7472751140594482
52 }
53]
54mdbc-rs [primary] sample_mflix>

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