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Vector Search

Use Views with MongoDB Vector Search

You can create a MongoDB 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 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:

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

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

Limitations

  • MongoDB Vector Search supports Views with the following stages:

    You can use the $expr operator only within the $match stage.

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

  • MongoDB 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.

  • 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 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.

To learn more, see Connect to a Cluster 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_voyage_4_large"
            },
            "missing"
          ]
        }
      }
    }
  ]
)
4

Create a MongoDB Vector Search index on the View.

db.moviesWithEmbeddings.createSearchIndex(
  "embeddingsIndex",
  "vectorSearch",
  {
    "fields": [
      {
        "type": "vector",
        "numDimensions": 2048,
        "path": "plot_embedding_voyage_4_large",
        "similarity": "cosine"
      }
    ]
  }
)
5

Run a query on the embeddingsIndex partial index.

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

    TIME_TRAVEL_EMBEDDINGS=[0.004048207309097052, -0.009754056110978127, 0.005103070288896561, -0.005260614678263664, -0.011836382560431957, 0.009648741222918034, 0.02159043960273266, 0.0001643942086957395, 0.005589403212070465, -0.015507853589951992, -0.018823135644197464, 0.010123942978680134, -0.03320762887597084, -0.004931826144456863, -0.02575509250164032, -0.031454090029001236, -0.0039934092201292515, -0.02076846733689308, -0.016631213948130608, -0.024576934054493904, 0.0069182561710476875, 0.01830255426466465, 0.01750798337161541, 0.031125303357839584, 0.013480325229465961, -0.01457628607749939, -0.00824710913002491, -0.025727692991495132, 0.010835889726877213, 0.022522006183862686, -0.000643877312541008, -0.003945461008697748, -0.005562004167586565, -0.026097580790519714, -0.0015343459090217948, 0.02817990630865097, -0.03523515909910202, 0.006425073370337486, -0.014055704697966576, -0.0033837808296084404, 0.011343199759721756, -0.04318087920546532, -0.035180360078811646, 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    This file contains the embeddings for the query term time travel.

  2. In mongosh, load the file into mongosh to use the embeddings in your query:

    load('/<path-to-file>/query-embeddings.js');

  3. Run the following query:

    1use sample_mflix
    2db.moviesWithEmbeddings.aggregate([
    3  {
    4    "$vectorSearch": {
    5      "index": "embeddingsIndex",
    6      "path": "plot_embedding_voyage_4_large",
    7      "queryVector": TIME_TRAVEL_EMBEDDINGS,
    8      "numCandidates": 100,
    9      "limit": 10
    10    }
    11  },
    12  {
    13    "$project": {
    14      "_id": 0,
    15      "plot": 1,
    16      "title": 1,
    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...',
        title: 'A.P.E.X.'
      },
      {
        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 reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
        title: 'Thrill Seekers'
      },
      {
        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 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 psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
        title: 'Retroactive'
      },
      {
        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: "My iz budushchego, or We Are from the Future, is a movie about time travel. Four 21st century treasure seekers are transported back into the middle of a WWII battle in Russia. The movie's ...",
        title: 'My iz budushchego'
      },
      {
        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: "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'
      }
    ]
1

Connect to your cluster using mongosh.

To learn more, see Connect to a Cluster 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_voyage_4_large"
            },
            "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. In the sidebar, click Clusters under the Database heading.

The Clusters page displays.

5

In Atlas, go to the Search & Vector Search page for your cluster.

You can go the MongoDB Search page from the Search & Vector Search option, or the Data Explorer.

6

Click Create Search Index.

7

Start your index configuration.

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

Search Type

Select the Vector Search index type.

How do you want to set up your vector data?

Select one of the following:

  • Automated Embedding if you want MongoDB to generate and manage vector embeddings for your text fields.

  • Bring your own embeddings if you have already generated vector embeddings for your data.

Choose Bring your own embeddings.

Index Name and Data Source

Specify the following information:

  • Index Name: embeddingsIndex

  • Database and Collection:

    • sample_mflix

    • movies

Configuration Method

For a guided experience, select Visual Editor.

To edit the raw index definition, select JSON Editor.

Choose JSON Editor.

8

Specify an index definition.

  1. Specify the following index definition:

    1{
    2  "fields": [
    3    {
    4      "type": "vector",
    5      "numDimensions": 2048,
    6      "path": "plot_embedding_voyage_4_large",
    7      "similarity": "cosine"
    8    }
    9  ]
    10}

  2. Click Next.

9

Click Create Vector Search Index.

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

10

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

11

Check the status.

The newly created index displays on the Search & Vector Search page. While the index is building, the Status field reads Pending. When the index is finished building, the Status field reads Ready.

Note

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

12

Navigate to the view in the Atlas UI.

  1. Click the Browse Collections button for your cluster.

  2. Expand the sample_mflix database and click the moviesWithEmbeddings view.

13

Query the view.

  1. Go to Aggregation for the view.

  2. Click </> Text to switch to JSON view.

  3. Copy and paste the following query into the Query Editor

    1[
    2  {
    3    "$vectorSearch": {
    4      "index": "vector_index",
    5      "path": "plot_embedding_voyage_4_large",
    6      "queryVector": [0.004048207309097052, -0.009754056110978127, 0.005103070288896561, -0.005260614678263664, -0.011836382560431957, 0.009648741222918034, 0.02159043960273266, 0.0001643942086957395, 0.005589403212070465, -0.015507853589951992, -0.018823135644197464, 0.010123942978680134, -0.03320762887597084, -0.004931826144456863, -0.02575509250164032, -0.031454090029001236, -0.0039934092201292515, -0.02076846733689308, -0.016631213948130608, -0.024576934054493904, 0.0069182561710476875, 0.01830255426466465, 0.01750798337161541, 0.031125303357839584, 0.013480325229465961, -0.01457628607749939, -0.00824710913002491, -0.025727692991495132, 0.010835889726877213, 0.022522006183862686, -0.000643877312541008, -0.003945461008697748, -0.005562004167586565, -0.026097580790519714, -0.0015343459090217948, 0.02817990630865097, -0.03523515909910202, 0.006425073370337486, -0.014055704697966576, -0.0033837808296084404, 0.011343199759721756, -0.04318087920546532, -0.035180360078811646, 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    7      "numCandidates": 100,
    8      "limit": 10
    9    }
    10  },
    11  {
    12    "$project": {
    13      "_id": 0,
    14      "plot": 1,
    15      "title": 1,
    16    }
    17  }
    18]
    {
      "plot": "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": "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 reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.",
      "title": "Thrill Seekers"
    }
    {
      "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 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 psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.",
      "title": "Retroactive"
    }
    {
      "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": "My iz budushchego, or We Are from the Future, is a movie about time travel. Four 21st century treasure seekers are transported back into the middle of a WWII battle in Russia. The movie's ...",
      "title": "My iz budushchego"
    }
    {
      "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": "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"
    }
1

Connect to your MongoDB deployment using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_mflix database.

use sample_mflix
3

Create a moviesWithAutomatedEmbedding View.

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

Create a MongoDB Vector Search index on the View.

db.moviesWithAutomatedEmbedding.createSearchIndex(
  "autoEmbedIndex",
  "vectorSearch",
    {
      "fields": [
        {
          "type": "autoEmbed",
          "modality": "text",
          "path": "fullplot",
          "model": "voyage-4"
        }
      ]
    }
)
5

Run a query on the autoEmbedIndex partial index.

1use sample_mflix
2db.moviesWithAutomatedEmbedding.aggregate([
3  {
4    "$vectorSearch": {
5      "index": "autoEmbedIndex",
6      "path": "fullplot",
7      "query": {
8        "text": "time travel"
9      },
10      "numCandidates": 100,
11      "limit": 10
12    }
13  },
14  {
15    "$project": {
16      "_id": 0,
17      "fullplot": 1,
18      "title": 1,
19    }
20  }
21])
[
  {
    fullplot: "At the age of 21, Tim Lake (Domhnall Gleeson) discovers he can travel in time... The night after another unsatisfactory New Year party, Tim's father (Bill Nighy) tells his son that the men in his family have always had the ability to travel through time. Tim can't change history, but he can change what happens and has happened in his own life-so he decides to make his world a better place...by getting a girlfriend. Sadly, that turns out not to be as easy as you might think. Moving from the Cornwall coast to London to train as a lawyer, Tim finally meets the beautiful but insecure Mary (Rachel McAdams). They fall in love, then an unfortunate time-travel incident means he's never met her at all. So they meet for the first time again-and again-but finally, after a lot of cunning time-traveling, he wins her heart. Tim then uses his power to create the perfect romantic proposal, to save his wedding from the worst best-man speeches, to save his best friend from professional disaster and to get his pregnant wife to the hospital in time for the birth of their daughter, despite a nasty traffic jam outside Abbey Road. But as his unusual life progresses, Tim finds out that his unique gift can't save him from the sorrows and ups and downs that affect all families, everywhere. There are great limits to what time travel can achieve, and it can be dangerous too.",
    title: 'About Time'
  },
  {
    title: 'Freejack',
    fullplot: "Auto racer Alex Furlong is snatched by time travel, a split second before a fatal explosion, by Vasendak's 21st-century team of techies, who plan to sell his healthy body to an ailing rich man at McCandless Corporation, for a mind transfer. He escapes, but has no rights in this nightmare future of violence and sleaze. The story concerns his survival, and his attempt to revive his relationship with his fiancèe Julie, now 15 years older and an executive at McCandless."
  },
  {
    title: 'A.P.E.X.',
    fullplot: "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 plague ravaged alternate time-line whose war weary inhabitants are locked in a constant battle with killer robots which are automatically being sent there from Sinclair's lab. To escape this situation, Sinclair must find a similar time machine in this alternate world and prevent the disaster from ever happening."
  },
  {
    fullplot: 'Based on the classic sci-fi novel by H.G. Wells, scientist and inventor, Alexander Hartdegen, is determined to prove that time travel is possible. His determination is turned to desperation by a personal tragedy that now drives him to want to change the past. Testing his theories with a time machine of his own invention, Hartdegen is hurtled 800,000 years into the future, where he discovers that mankind has divided into the hunter - and the hunted.',
    title: 'The Time Machine'
  },
  {
    title: 'Timerider: The Adventure of Lyle Swann',
    fullplot: '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, thiefs and murderers....'
  },
  {
    fullplot: 'A ferry filled with crewmen from the USS Nimitz and their families was blown up in New Orleans on Mardi Gras. BATF Doug Carlin is brought in to assist in the massive investigation, and gets attached to an experimental FBI surveillance unit, one that uses spacefolding technology to directly look back a little over four days into the past. While tracking down the bomber, Carlin gets an idea in his head: could they use the device to actually travel back in time and not only prevent the bombing but also the murder of a local woman whose truck was used in the bombing?',
    title: 'Deja Vu'
  },
  {
    fullplot: "When the ability to travel through time is perfected, a new type of law enforcement agency is formed. It's called Time Enforcement Commission or TEC. A cop, Max Walker, is assigned to the group. On the day he was chosen, some men attack him and kill his wife. Ten years later Max is still grieving but has become a good agent for the TEC. He tracks down a former co-worker who went into the past to make money. Max brings him back for sentencing but not after telling Max that Senator McComb, the man in charge of TEC, sent him. Max has his eye on McComb.",
    title: 'Timecop'
  },
  {
    title: 'My iz budushchego',
    fullplot: "My iz budushchego, or We Are from the Future, is a movie about time travel. Four 21st century treasure seekers are transported back into the middle of a WWII battle in Russia. The movie's genres are action, drama, fantasy, and romance. The movie will leave you captivated."
  },
  {
    title: 'Retroactive',
    fullplot: "Karen has to take a ride in Frank's car. He's travelling with his wife, on his way to sell computer chips, which he obtained illegally. He suspects his wife is being unfaithful and tragic things happen, following a discussion. Karen escapes, reaching a lab where a scientist has discovered how to go back in time, and she returns by accident some minutes before the violent events. Then she tries to make things better, but things tend to become worse, as time cycles keep being repeated."
  },
  {
    fullplot: "After breaking out of a moon-based maximum security prison, Boris the Animal decides to go back in time and eliminate the person who arrested him - Agent K. When he does so, Agent J realizes that the time line has been changed and he too travels back to July 15, 1969, the day before Agent K is killed. After overcoming some disbelief, J manages to convince K and others of just who he is and why he's there. With the help of a being who can see all time lines, they track Boris down. J also learns a secret, something K had never told him.",
    title: 'Men in Black 3'
  }
]
1

Connect to your MongoDB deployment using mongosh.

To learn more, see Connect to a Cluster via mongosh.

2

Switch to the sample_mflix database.

use sample_mflix
3

Create a moviesWithAutomatedEmbedding View.

db.createView(
  "moviesWithAutomatedEmbedding",
  "embedded_movies",
  [
    {
      "$match": {
        "$expr": {
          "$ne": [
            {
              "$type": "$fullplot"
            },
            "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. In the sidebar, click Clusters under the Database heading.

The Clusters page displays.

5

In Atlas, go to the Search & Vector Search page for your cluster.

You can go the MongoDB Search page from the Search & Vector Search option, or the Data Explorer.

6

Click Create Search Index.

7

Start your index configuration.

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

Search Type

Select the Vector Search index type.

How do you want to set up your vector data?

Select one of the following:

  • Automated Embedding if you want MongoDB to generate and manage vector embeddings for your text fields.

  • Bring your own embeddings if you have already generated vector embeddings for your data.

Choose Automated Embedding.

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.

Choose JSON Editor.

8

Create a MongoDB Vector Search index on the View.

  1. Specify the following index definition:

    {
      "fields": [
        {
          "type": "autoEmbed",
          "modality": "text",
          "path": "fullplot",
          "model": "voyage-4"
        }
      ]
    }

  2. Click Next.

9

Click Create Vector Search Index.

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

10

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

11

Check the status.

The newly created index displays on the Search & Vector Search page. While the index is building, the Status field reads Pending. When the index is finished building, the Status field reads Ready.

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 autoEmbedIndex partial index.

  1. Click the Query button to the right of the index to query.

  2. Copy and paste the following query into the Query Editor and click the Search button to run the query.

    1[
    2  {
    3    "$vectorSearch": {
    4      "index": "autoEmbedIndex",
    5      "path": "fullplot",
    6      "query": {
    7        "text": "time travel"
    8      },
    9      "numCandidates": 100,
    10      "limit": 10
    11    }
    12  }
    13]
    SCORE: 0.7573064565658569   _id:  “573a13d8f29313caabda6557”
        awards: Object
        cast: Array (4)
        countries: Array (1)
    SCORE: 0.7563554048538208   _id:  “573a1399f29313caabced116”
        awards: Object
        cast: Array (4)
        countries: Array (1)
    SCORE: 0.753066897392273    _id:  “573a1399f29313caabcee36f”
        awards: Object
        cast: Array (4)
        countries: Array (1)
    SCORE: 0.7522954940795898   _id:  “573a13a5f29313caabd13b4b”
        awards: Object
        cast: Array (4)
        countries: Array (1)
    SCORE: 0.7425765991210938   _id:  “573a1398f29313caabce8dc3”
        awards: Object
        cast: Array (4)
        countries: Array (1)
    SCORE: 0.735001802444458    _id:  “573a13b4f29313caabd3eeb2”
        awards: Object
        cast: Array (4)
        countries: Array (2)
    SCORE: 0.7344385385513306   _id:  “573a1399f29313caabceec0e”
        awards: Object
        cast: Array (4)
        countries: Array (3)
    SCORE: 0.7337626814842224   _id:  “573a13c1f29313caabd635b3”
        awards: Object
        cast: Array (4)
        countries: Array (1)
    SCORE: 0.7334108948707581   _id:  “573a139af29313caabcf0316”
        awards: Object
        cast: Array (4)
        countries: Array (1)
    SCORE: 0.7332584857940674   _id:  “573a13c6f29313caabd715d3”
        awards: Object
        cast: Array (4)
        countries: Array (1)

Performance Considerations

Highly complex view transformations can increase indexing and query time. This is because the mongod must read the view definition when it filters and transforms the oplog entries during indexing (initial sync and steady state replication), and when it applies those transformations to the returned documents at query-time.

Consider creating a materialized view to avoid extra replication load on Atlas. You can also query the source collection directly to avoid query latency from the view transformation.

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 MongoDB Vector Search.

  • You edit a View in a way that does not meet the MongoDB 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 MongoDB Vector Search index on a View, the mongot process performs the same tasks as when you create an MongoDB Vector Search index on a regular collection. The mongot process:

  1. Creates MongoDB 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 MongoDB Vector Search indexes.

  3. Processes MongoDB 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 MongoDB Vector Search index on a View, the View definition is applied during Step 1 and 2, and the transformed documents are indexed based on the search index definition, then stored on disk.

Learn More

To learn more about Views, see Views.

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

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