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Atlas Vector Search

Use MongoDB Views to Transform Documents and Filter Collections for Atlas Vector Search

On this page

  • Requirements
  • Limitations
  • Example: Filter Documents
  • Troubleshoot
  • Index Process
  • Learn More

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.

Note

Using MongoDB Views with Atlas Vector Search is available as a Preview feature. The feature and the corresponding documentation might change at any time during the Preview period.

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.

During the Preview period, you must use:

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 variables that produce dynamic results like $$USER_ROLES and $random.

  • 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.

1

Connect to the Atlas cluster using mongosh.

To learn more, see Connect via mongosh.

2

Switch to the sample_mflix database.

use sample_mflix
3

Create a moviesWithEmbeddings View.

db.createView(
  "moviesWithEmbeddings",
  "embedded_movies",
  [
    {
      "$match": {
        "$expr": {
          "$ne": [
            {
              "$type": "$plot_embedding"
            },
            "missing"
          ]
        }
      }
    }
  ]
)
4

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

  1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

  3. If it's not already displayed, click Clusters in the sidebar.

    The Clusters page displays.

5

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.

  1. In the sidebar, click Atlas Search under the Services heading.

    Note

    If you have no clusters, click Create cluster to create one. To learn more, see Create a Cluster.

  2. From the Select data source dropdown, select your cluster and click Go to Atlas Search.

    The Atlas Search page displays.

  1. Click the Browse Collections button for your cluster.

  2. Expand the database and select the collection.

  3. Click the Search Indexes tab for the collection.

    The Atlas Search page displays.

  1. Click the cluster's name.

  2. Click the Atlas Search tab.

    The Atlas Search page displays.

6

Click Create Search Index.

7

Click Create Search Index.

8

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.
9

Specify an index definition.

  1. Specify the following index definition:

    {
      "fields": [
        {
          "type": "vector",
          "numDimensions": 1536,
          "path": "plot_embedding",
          "similarity": "cosine"
        }
      ]
    }

  2. Click Next.

10

Click Create Search Index.

Atlas displays a Toast (brief, non-interactive notification) to let you know your index is building.

11

Check the status.

The newly created index appears 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.

12

Run a query on the embeddingsIndex partial index.

Note

The following example queries the embeddingsIndex index by running the .aggregate command against the source collection embedded_movies. During the Preview period, you must query search indexes created on Views using the source collection (embedded_movies) of the View. If you query the View (moviesWithEmbeddings), Atlas Vector Search returns no results.

use sample_mflix
1db.embedded_movies.aggregate([
2  {
3    "$vectorSearch": {
4      "index": "embeddingsIndex",
5      "path": "plot_embedding",
6      "queryVector": 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[
  {
    "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" : "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" : "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" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
    "title" : "Crusade in Jeans"
  },
  {
    "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" : "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" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
    "title" : "Men in Black 3"
  },
  {
    "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.",
    "title" : "Tomorrowland"
  },
  {
    "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.",
    "title" : "Love Story 2050"
  },
  {
    "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...",
    "title" : "The Portal"
  }
]

Troubleshoot

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 stall 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. However, the index is queryable until the replication is automatically removed from the oplog.

  • 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.

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:

  1. Creates Atlas Vector Search indexes based on the rules in the index definition for the collection.

  2. Monitors change streams for the current state of the documents and indexes for the collections for which you defined the Atlas Vector Search indexes.

  3. 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 MongoDB Views to Transform Documents and Filter Collections for Atlas Search.

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