Use Views with Atlas Vector Search

You can create an Atlas Vector Search index on a View 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 sample database.

Note

Disambiguation

This page discusses standard views. To learn about on-demand materialized views, see On-Demand Materialized Views.

To learn about the differences between the view types, see Comparison with On-Demand Materialized Views.

Requirements

You must use MongoDB 8.0 or higher.

On MongoDB v8.0+:
- Use the Atlas UI or the Atlas Administration API to create Atlas Vector Search indexes on Views.
- Run the Atlas Vector Search queries against the source collection. Reference the Atlas 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 and Driver methods, db.collection.createSearchIndex(), db.collection.updateSearchIndex(), db.collection.dropSearchIndex(), and $listSearchIndexes to create and manage Atlas Vector Search indexes on Views.
- Run the Atlas Vector Search queries against the View.

To edit a View, you must have a User Admin role and use the collMod database command.

Limitations

Atlas Vector Search supports Views only for $expr in the following stages:
Index names must be unique across a source collection and all of its Views.
Atlas Vector Search doesn't support view definitions with operators that produce dynamic results, such as the $$USER_ROLES system variable and the $rand aggregation operator.
Atlas 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 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.

Connect to the Atlas cluster using `mongosh`.

To learn more, see Connect via mongosh.

Switch to the `sample_mflix` database.

use sample_mflix

Create a `moviesWithEmbeddings` View.

db.createView(
  "moviesWithEmbeddings",
  "embedded_movies",
  [
    {
      "$match": {
        "$expr": {
          "$ne": [
            {
              "$type": "$plot_embedding_voyage_3_large"
            },
            "missing"
          ]
        }
      }
    }
  ]
)

In Atlas, go to the Clusters page for your project.

WARNING: Navigation Improvements In Progress We're currently rolling out a new and improved navigation experience. If the following steps don't match your view in the Atlas UI, see the preview documentation.

If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.
If it's not already displayed, select your desired project from the Projects menu in the navigation bar.
If it's not already displayed, click Clusters in the sidebar.
The Clusters page displays.

Go to the Atlas Search page for your cluster.

You can go the Atlas Search page from the sidebar, the Data Explorer, or your cluster details page.

In the sidebar, click Atlas Search under the Services heading.
If you have no clusters, click Create cluster to create one. To learn more, see Create a Cluster.
If your project has multiple clusters, select the cluster you want to use from the Select cluster dropdown, then click Go to Atlas Search.
The Atlas Search page displays.

Click the Browse Collections button for your cluster.
Expand the database and select the collection.
Click the Search Indexes tab for the collection.
The Atlas Search page displays.

Click the cluster's name.
Click the Atlas Search tab.
The Atlas Search page displays.

Click Create Search Index.

Start your index configuration.

Make the following selections on the page and then click Next.

Search Type	Select the Atlas Vector Search index type.
Index Name and Data Source	Specify the following information: Index Name: `embeddingsIndex` Database and Collection: `sample_mflix` `moviesWithEmbeddings`
Configuration Method	For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor.

Note

Your Atlas Search index is named default by default. If you keep this name, then your index will be the default Search index for any Atlas Search query that does not specify a different index option in its operators. If you are creating multiple indexes, we recommend that you maintain a consistent, descriptive naming convention across your indexes.

Specify an index definition.

Specify the following index definition:
```
{
  "fields": [
    {
      "type": "vector",
      "numDimensions": 2048,
      "path": "plot_embedding_voyage_3_large",
      "similarity": "cosine"
    }
  ]
}
```
Click Next.

Click Create Vector Search Index.

Atlas displays a modal window to let you know your index is building.

Close the You're All Set! Modal Window by clicking the Close button.

Check the status.

The newly created index displays on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.

Note

Larger collections take longer to index. You will receive an email notification when your index is finished building.

Run a query on the `embeddingsIndex` partial index.

Note

The following example queries the embeddingsIndex index by running the .aggregate command against the View named moviesWithEmbeddings. If your cluster is running MongoDB v8.0, you must query the source collection (for example, embedded_movies) using the index on the View. Upgrade to MongoDB v8.1+ to query the View directly.

use sample_mflix

1 db.moviesWithEmbeddings.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "embeddingsIndex",
5       "path": "plot_embedding_voyage_3_large",
6       "queryVector": 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7       "numCandidates": 100,
8       "limit": 10
9     }
10   },
11   {
12     "$project": {
13       "_id": 0,
14       "plot": 1,
15       "title": 1,
16     }
17   }
18 ])

[
  {
    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.',
    title: 'About Time'
  },
  {
    plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
    title: 'Retroactive'
  },
  {
    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...',
    title: 'A.P.E.X.'
  },
  {
    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.',
    title: 'Timecop'
  },
  {
    plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.',
    title: 'Back to the Future Part II'
  },
  {
    plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
    title: 'Thrill Seekers'
  },
  {
    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, ...',
    title: 'Timerider: The Adventure of Lyle Swann'
  },
  {
    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.',
    title: 'The Time Machine'
  },
  {
    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.',
    title: "The Time Traveler's Wife"
  },
  {
    plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.',
    title: 'The Final Countdown'
  }
]

Performance Considerations

For indexes created on views, Atlas Vector Search applies the transformations or filters that you define in the view pipeline at query-time and at replication-time. While trivial transformations or filters in the view pipeline typically result in negligible performance impact, complex transformations or highly selective filters in the view pipeline might significantly slow down querying or replication. If you want to perform a heavy transformation on the documents or apply a highly selective filter, consider creating a materialized view with already transformed or filtered documents. You can evaluate view performance by comparing query latency executed against the view and against its source collection.

Troubleshoot

Indexes Change to `FAILED`

Indexes change to the FAILED status in the following scenarios:

You create an index on a View that is incompatible with Atlas Vector Search.
You edit a View in a way that does not meet the Atlas Vector Search compatibility requirements.
You remove or change a View's source collection.
For example, if one View is created on another View, and you change the parent View source to another collection.
Note
This limitation also applies if a View is a descendent of other Views. For example, you can't change or remove the source collection that all descendents originate from.

Indexes Change to `STALE`

Indexes change to the STALE status in the following scenarios:

Warning

If the aggregation pipeline defined in your View is incompatible with the documents in the collection, search replication fails. For example, if a $toDouble expression operates on a document field that contains an array, the replication fails. Ensure your View works with all documents in the collection without errors.

If the View definition causes an aggregation failure while an index is READY, the index becomes STALE. The index will return to READY after you resolve the document or change the view definition so that it doesn't fail anymore. When STALE, the index remains queryable. If the index falls off the oplog, an index rebuild is triggered.
If the View definition causes an aggregation pipeline failure while the index is BUILDING, the index build is stuck until you fix the document. The index will return to READY after you resolve the document or change the view definition so that it doesn't fail anymore.

You can view index statuses in the Atlas UI on the index status details page.

Error: `$search` is only valid as the first stage in a pipeline

This error appears when you query a view using a MongoDB version before 8.1.

If you use a MongoDB version before 8.0, we recommend you upgrade to 8.1+ to query the view directly. You can upgrade to 8.0 to query the source collection.
If you use MongoDB 8.0, you must query the view index against the source collection. For example, run .aggregate() on the collection instead of the view.

Index Process

When you create an Atlas Vector Search index on a View, the mongot process performs the same tasks as when you create an Atlas Vector Search index on a regular collection. The mongot process:

Creates Atlas Vector Search indexes based on the rules in the index definition for the collection.
Monitors change streams for the current state of the documents and indexes for the collections for which you defined the Atlas Vector Search indexes.
Processes Atlas Vector Search queries and returns the document IDs and other search metadata for the matching documents to mongod, which then does a full document lookup and returns the results to the client.

When you create an Atlas Vector Search index on a View, the View definition is applied during Step 1 and 2, and the transformed documents are stored in the Atlas Vector Search index on disk.

Learn More

To learn more about Views, see Views.

To create an Atlas Search index on a View, see Use Views with Atlas Search.

Back

Combined Vector Search

Vector Quantization

Note

Disambiguation

Requirements

Limitations

Example: Filter Documents

Connect to the Atlas cluster using mongosh.

Switch to the sample_mflix database.

Create a moviesWithEmbeddings View.

In Atlas, go to the .leafygreen-ui-8n27nz{font-style:normal;font-weight:700;}Clusters page for your project.

Go to the Atlas Search page for your cluster.

Click Create Search Index.

Click Create Search Index.

Start your index configuration.

Note

Specify an index definition.

Click Create Vector Search Index.

Close the You're All Set! Modal Window by clicking the Close button.

Check the status.

Note

Run a query on the embeddingsIndex partial index.

Note

Performance Considerations

Troubleshoot

Indexes Change to FAILED

Note

Indexes Change to STALE

Warning

Error: $search is only valid as the first stage in a pipeline

Index Process

Learn More

Earn a Skill Badge

Connect to the Atlas cluster using `mongosh`.

Switch to the `sample_mflix` database.

Create a `moviesWithEmbeddings` View.

In Atlas, go to the Clusters page for your project.

Run a query on the `embeddingsIndex` partial index.

Indexes Change to `FAILED`

Indexes Change to `STALE`

Error: `$search` is only valid as the first stage in a pipeline