Use Views with MongoDB Vector Search

For the complete MongoDB documentation index, see www.mongodb.com/docs/llms.txt

Use Views with MongoDB Vector Search

You can create a MongoDB Vector Search index on a [View](https://www.mongodb.com/docs/manual/core/views.md#std-label-views-landing-page to transform documents and collections so that you can partially index a collection, support incompatible data types or data models, and more.

The following examples use the [sample_mflix](https://www.mongodb.com/docs/manual/sample-data/sample-mflix.md#std-label-sample-mflix sample database.

Note: Disambiguation

This page discusses standard views. To learn about on-demand materialized views, see [On-Demand Materialized Views.](https://www.mongodb.com/docs/manual/core/materialized-views.md#std-label-manual-materialized-views

To learn about the differences between the view types, see [Comparison with On-Demand Materialized Views.](https://www.mongodb.com/docs/manual/core/views.md#std-label-view-materialized-view-compare

Requirements

You must use MongoDB 8.0 or higher.

  • On MongoDB v8.0+:

  • Use the Atlas UI or the Atlas Administration API to create MongoDB Vector Search indexes on Views.

  • Run the MongoDB Vector Search queries against the source collection. Reference the MongoDB Vector Search index that was created on the View. These queries return the original documents as they appear in the source collection.

  • On MongoDB v8.1+, you can additionally:

  • Use [mongosh](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh and Driver methods, [db.collection.createSearchIndex()](https://www.mongodb.com/docs/manual/reference/method/db.collection.createSearchIndex.md#mongodb-method-db.collection.createSearchIndex, [db.collection.updateSearchIndex()](https://www.mongodb.com/docs/manual/reference/method/db.collection.updateSearchIndex.md#mongodb-method-db.collection.updateSearchIndex, [db.collection.dropSearchIndex()](https://www.mongodb.com/docs/manual/reference/method/db.collection.dropSearchIndex.md#mongodb-method-db.collection.dropSearchIndex, and [$listSearchIndexes](https://www.mongodb.com/docs/manual/reference/operator/aggregation/listSearchIndexes.md#mongodb-pipeline-pipe.-listSearchIndexes to create and manage MongoDB Vector Search indexes on Views.

  • Run the MongoDB Vector Search queries against the View.

  • On MongoDB v8.2+, you can additionally:

  • Use [$rankFusion](https://www.mongodb.com/docs/manual/reference/operator/aggregation/rankFusion.md#mongodb-pipeline-pipe.-rankFusion in aggregation pipelines on Views.

  • Query Views that include sharded sub-pipelines with [$lookup](https://www.mongodb.com/docs/manual/reference/operator/aggregation/lookup.md#mongodb-pipeline-pipe.-lookup and [$unionWith.](https://www.mongodb.com/docs/manual/reference/operator/aggregation/unionWith.md#mongodb-pipeline-pipe.-unionWith

To edit a View, you must have a [User Admin](https://www.mongodb.com/docs/manual/tutorial/configure-scram-client-authentication.md#std-label-create-user-admin role and use the [collMod](https://www.mongodb.com/docs/manual/reference/command/collMod.md#mongodb-dbcommand-dbcmd.collMod database command.

Limitations

  • MongoDB Vector Search supports Views with the following stages:

  • [$addFields](https://www.mongodb.com/docs/manual/reference/operator/aggregation/addFields.md#mongodb-pipeline-pipe.-addFields

  • [$set](https://www.mongodb.com/docs/manual/reference/operator/aggregation/set.md#mongodb-pipeline-pipe.-set

  • [$match](https://www.mongodb.com/docs/manual/reference/operator/aggregation/match.md#mongodb-pipeline-pipe.-match

You can use the [$expr](https://www.mongodb.com/docs/manual/reference/operator/query/expr.md#mongodb-query-op.-expr operator only within the [$match](https://www.mongodb.com/docs/manual/reference/operator/aggregation/match.md#mongodb-pipeline-pipe.-match stage.

  • Index names must be unique across a source collection and all of its Views.

  • MongoDB Vector Search doesn't support [view definitions](https://www.mongodb.com/docs/manual/core/views.md#std-label-views-landing-page with operators that produce dynamic results, such as the [$$USER_ROLES](https://www.mongodb.com/docs/manual/reference/aggregation-variables/#mongodb-variable-variable.USER_ROLES system variable and the [$rand](https://www.mongodb.com/docs/manual/reference/operator/aggregation/rand.md#mongodb-expression-exp.-rand aggregation operator.

  • MongoDB Vector Search queries return the original documents as they appear in the source collection.

Example: Filter Documents

To create a View, you must have the [createCollection](https://www.mongodb.com/docs/manual/reference/privilege-actions.md#mongodb-authaction-createCollection privilege.

You can filter documents to partially index a collection. The following example creates a View on the sample_mflix.embedded_movies collection so that only documents that have a vector embedding field are indexed.

You can filter documents to partially index a collection. The following example creates a View on the sample_mflix.embedded_movies collection so that only documents that have the field for which you want to automatically generate vector embeddings are indexed.

  1. Connect to your MongoDB deployment using [mongosh](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh.

To learn more, see [Connect to a Cluster via mongosh.](https://www.mongodb.com/docs/atlas/mongo-shell-connection.md#std-label-connect-mongo-shell

  1. Switch to the sample_mflix database.

sh use sample_mflix

  1. Create a moviesWithEmbeddings View.

javascript db.createView( "moviesWithEmbeddings", "embedded_movies", [ { "$match": { "$expr": { "$ne": [ { "$type": "$plot_embedding_voyage_4_large" }, "missing" ] } } } ] )

  1. Create a MongoDB Vector Search index on the View.

javascript db.moviesWithEmbeddings.createSearchIndex( "embeddingsIndex", "vectorSearch", { "fields": [ { "type": "vector", "numDimensions": 2048, "path": "plot_embedding_voyage_4_large", "similarity": "cosine" } ] } )

  1. Run a query on the embeddingsIndex partial index.

Save the following embeddings in a file named query-embeddings.js:

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