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Use Atlas Search with an Atlas Deployment

On this page

  • Complete the Prerequisites
  • Review the Limitations
  • Create an Atlas Search Index and Run a Query
  • Use Atlas Vector Search with an Atlas Deployment
  • Supported Actions

This tutorial shows you how to use the atlas deployments command to create an Atlas Search index, run a query, and work with Atlas Vector Search.

Complete the Prerequisites

Before you begin, complete the following prerequisites:

Important

For compatibility information on each product in the dependencies list, see the product's installation documentation.

  • Install the Atlas CLI.

    Example (MacOS):

    brew install mongodb-atlas-cli

    To learn about the Atlas CLI install instructions for other operating systems, see Install or Update the Atlas CLI.

  • Install mongosh version 2.0 or later.

    Example (MacOS):

    brew install mongosh

    To learn about the mongosh install instructions for other operating systems, see Install mongosh.

  • (Optional) Install Compass version 1.39.4 or later.

    Example (MacOS):

    brew install mongodb-compass

    To learn about the Compass install instructions for other operating systems, see Download and Install Compass.

  • Install MongoDB Database Tools.

    Example (MacOS):

    brew tap mongodb/brew && brew install mongodb-database-tools

    To learn about the MongoDB Database Tools install instructions for other operating systems, see Installing the Database Tools.

  • Install Docker.

    Note

    Docker requires a network connection for pulling and caching MongoDB images.

    Podman v5.0+ is also supported for Linux RHEL versions.

  • Create an Atlas deployment.

    Example:

    atlas deployments setup

    To learn more, see Create a Local Atlas Deployment.

    Important

    To use the Atlas CLI with Atlas Vector Search, you must create an Atlas deployment with MongoDB 7.0.5 or later. If you created a local Atlas deployment with an earlier MongoDB version, you don't get the latest MongoDB version automatically. You must delete the earlier images and deployments. Then you must create a new Atlas deployment.

  • Download and load the sample data.

    Example:

    1. Run the following command to download the sample data:

      curl  https://atlas-education.s3.amazonaws.com/sampledata.archive -o sampledata.archive

    2. Copy and paste the following command into your terminal and replace {port-number} with the port for your deployment:

      mongorestore --archive=sampledata.archive --port={port-number}

Review the Limitations

The following limitations apply:

  • You can't perform concurrent search.

  • You can't exceed 1024 boolean clauses.

Create an Atlas Search Index and Run a Query

Use the atlas deployments command to work with Atlas Search. To learn more, see Atlas Search.

You can run this command in the following ways:

  • Interactive Mode: the command prompts you for the Atlas Search settings.

    Note

    Interactive Mode allows you to create a basic Atlas Search index with default settings.

  • Non-Interactive Mode (Flags): you run the command with the specified options. The command does not prompt you to provide further values.

  • Non-Interactive Mode (Configuration File): you run the command with the specified settings in the configuration file. The command does not prompt you to provide further values.

Click one of the following tabs to see the command for your preferred mode.

1

Create an Atlas Search index.

  1. Run the following command:

    atlas deployments search indexes create

  2. Specify the deployment and press Enter..

  3. Specify searchIndex as the name for the index and press Enter..

  4. Specify the sample_mflix database and press Enter..

  5. Specify the movies collection and press Enter..

2

Connect to the deployment.

  1. Run the following command to connect to a deployment:

    atlas deployments connect

  2. Specify the deployment to connect to and press Enter..

  3. Specify mongosh and press Enter..

3

Switch to the sample_mflix database.

use sample_mflix
4

Run an example query.

db.movies.aggregate([
  {
    $search: {
      "index": "searchIndex",
      "text": {
        "query": "baseball",
        "path": "plot"
      }
    }
  },
  {
    $limit: 5
  },
  {
    $project: {
      "_id": 0,
      "title": 1,
      "plot": 1
    }
  }
])
[
  {
    plot: 'A trio of guys try and make up for missed opportunities in childhood by forming a three-player baseball team to compete against standard children baseball squads.',
    title: 'The Benchwarmers'
  },
  {
    plot: 'A young boy is bequeathed the ownership of a professional baseball team.',
    title: 'Little Big League'
  },
  {
    plot: 'A trained chimpanzee plays third base for a minor-league baseball team.',
    title: 'Ed'
  },
  {
    plot: 'The story of the life and career of the famed baseball player, Lou Gehrig.',
    title: 'The Pride of the Yankees'
  },
  {
    plot: 'Babe Ruth becomes a baseball legend but is unheroic to those who know him.',
    title: 'The Babe'
  }
]
1

Create an Atlas Search index.

Copy and paste the following command into your terminal and replace {myRs} with your deployment:

atlas deployments search indexes create searchIndex --deploymentName {myRs} --db sample_mflix --collection movies
2

Connect to the deployment.

  1. Run the following command to connect to a deployment:

    atlas deployments connect

  2. Specify the deployment to connect to and press Enter..

  3. Specify mongosh and press Enter..

3

Switch to the sample_mflix database.

use sample_mflix
4

Run an example query.

db.movies.aggregate([
  {
    $search: {
      "index": "searchIndex",
      "text": {
        "query": "baseball",
        "path": "plot"
      }
    }
  },
  {
    $limit: 5
  },
  {
    $project: {
      "_id": 0,
      "title": 1,
      "plot": 1
    }
  }
])
[
  {
    plot: 'A trio of guys try and make up for missed opportunities in childhood by forming a three-player baseball team to compete against standard children baseball squads.',
    title: 'The Benchwarmers'
  },
  {
    plot: 'A young boy is bequeathed the ownership of a professional baseball team.',
    title: 'Little Big League'
  },
  {
    plot: 'A trained chimpanzee plays third base for a minor-league baseball team.',
    title: 'Ed'
  },
  {
    plot: 'The story of the life and career of the famed baseball player, Lou Gehrig.',
    title: 'The Pride of the Yankees'
  },
  {
    plot: 'Babe Ruth becomes a baseball legend but is unheroic to those who know him.',
    title: 'The Babe'
  }
]
1

Create an indexDef.json configuration file.

{
  "collectionName": "movies",
  "database": "sample_mflix",
  "mappings": {
    "dynamic": true
  },
  "name": "searchIndex"
}
2

Create an Atlas Search index.

  1. Run the following command:

    atlas deployments search indexes create --file indexDef.json

  2. Specify the deployment and press Enter..

3

Connect to the deployment.

  1. Run the following command to connect to a deployment:

    atlas deployments connect

  2. Specify the deployment to connect to and press Enter..

  3. Specify mongosh and press Enter..

4

Switch to the sample_mflix database.

use sample_mflix
5

Run an example query.

db.movies.aggregate([
  {
    $search: {
      "index": "searchIndex",
      "text": {
        "query": "baseball",
        "path": "plot"
      }
    }
  },
  {
    $limit: 5
  },
  {
    $project: {
      "_id": 0,
      "title": 1,
      "plot": 1
    }
  }
])
[
  {
    plot: 'A trio of guys try and make up for missed opportunities in childhood by forming a three-player baseball team to compete against standard children baseball squads.',
    title: 'The Benchwarmers'
  },
  {
    plot: 'A young boy is bequeathed the ownership of a professional baseball team.',
    title: 'Little Big League'
  },
  {
    plot: 'A trained chimpanzee plays third base for a minor-league baseball team.',
    title: 'Ed'
  },
  {
    plot: 'The story of the life and career of the famed baseball player, Lou Gehrig.',
    title: 'The Pride of the Yankees'
  },
  {
    plot: 'Babe Ruth becomes a baseball legend but is unheroic to those who know him.',
    title: 'The Babe'
  }
]

Use Atlas Vector Search with an Atlas Deployment

Use the atlas deployments command to work with Atlas Vector Search. To learn more, see How to Index Vector Embeddings for Vector Search.

Important

To use the Atlas CLI with Atlas Vector Search, you must create an Atlas deployment with MongoDB 7.0.5 or later. If you created a local Atlas deployment with an earlier MongoDB version, you don't get the latest MongoDB version automatically. You must delete the earlier images and deployments. Then you must create a new Atlas deployment.

1

Create an indexDef-vector.json configuration file.

{
  "name": "vectorSearchIndex",
  "type": "vectorSearch",
  "collectionName": "embedded_movies",
  "database": "sample_mflix",
  "fields": [
    {
      "type": "vector",
      "path": "plot_embedding",
      "numDimensions": 1536,
      "similarity": "euclidean"
    }
  ]
}
2

Create an Atlas Search index.

  1. Run the following command:

    atlas deployments search indexes create --file indexDef-vector.json

  2. Specify the deployment and press Enter..

3

Connect to the deployment.

  1. Run the following command to connect to a deployment:

    atlas deployments connect

  2. Specify the deployment to connect to and press Enter..

  3. Specify mongosh and press Enter..

4

Switch to the sample_mflix database.

use sample_mflix
5

Run an example query.

Important

Copying and pasting a queryVector field with many numbers into your terminal might take a while depending on your machine.

db.embedded_movies.aggregate([
  {
    "$vectorSearch": {
      "index": "vectorSearchIndex",
      "path": "plot_embedding",
      "queryVector": 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    plot: 'When two girls move to the country to be near their ailing mother, they have adventures with the wonderous forest spirits who live nearby.',
    title: 'My Neighbor Totoro',
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    title: 'Black Moon',
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    title: 'Forest Warrior',
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    plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.',
    title: 'Coraline',
    score: 0.815956711769104
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    plot: "Set in a medieval village that is haunted by a werewolf, a young girl falls for an orphaned woodcutter, much to her family's displeasure.",
    title: 'Red Riding Hood',
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  },
  {
    plot: 'Their women having been enslaved by the local pack of lesbian vampires thanks to an ancient curse, the remaining menfolk of a rural town send two hapless young lads out onto the moors as a sacrifice.',
    title: 'Vampire Killers',
    score: 0.8102427124977112
  },
  {
    plot: 'In a mythical land, a man and a young boy investigate a series of unusual occurrences.',
    title: 'Tales from Earthsea',
    score: 0.8100577592849731
  },
  {
    plot: 'Because of the actions of her irresponsible parents, a young girl is left alone on a decrepit country estate and survives inside her fantastic imagination.',
    title: 'Tideland',
    score: 0.8080059885978699
  },
  {
    plot: 'Two siblings discover a supernatural escape from a troubled home, but find their bond tested when reality threatens to tear their family apart.',
    title: 'One and Two',
    score: 0.8072565197944641
  }
]

Supported Actions

To learn all of the actions that atlas deployments supports, see atlas deployments.

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