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Usa MongoDB Search y búsqueda vectorial con Atlas

Este tutorial muestra cómo utilizar el atlas deployments comando para crear un índice de búsqueda de MongoDB Search, ejecutar una query y trabajar con MongoDB Vector Search.

Completar los requisitos previos

Antes de comenzar, completa los siguientes requisitos previos:

Importante

Para obtener información sobre la compatibilidad de cada producto en la lista de dependencias, consulte la documentación de instalación del producto.

  • Instala el Atlas CLI.

    Ejemplo (MacOS):

    brew install mongodb-atlas-cli

    Para aprender sobre las instrucciones de instalación de Atlas CLI para otros sistemas operativos, consulta Instalar o actualizar Atlas CLI.

  • Instalar mongosh versión 2.0 o posterior.

    Ejemplo (MacOS):

    brew install mongosh

    Para aprender sobre las instrucciones de instalación de mongosh para otros sistemas operativos, consulta Instalar mongosh.

  • (Opcional) Instala la versión de Compass 1.39.4 o posterior.

    Ejemplo (MacOS):

    brew install mongodb-compass

    Para aprender sobre las instrucciones de instalación de Compass en otros sistemas operativos, consulte Descargar e instalar Compass.

  • Instale MongoDB Database Tools.

    Ejemplo (MacOS):

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

    Para obtener información sobre las instrucciones de instalación de MongoDB Database Tools en otros sistemas operativos, consulta Instalación de las Database Tools.

  • Install Docker.

    Nota

    Docker requiere una conexión de red para descargar y almacenar en caché las imágenes de MongoDB.

    Podman v5.0+ también es compatible con las versiones de Linux RHEL.

  • Crea una implementación de Atlas.

    Ejemplo:

    atlas deployments setup

    Para aprender más, consulta Crear una implementación local de Atlas.

    Importante

    Para utilizar la Atlas CLI con MongoDB Vector Search, debe crear una implementación de Atlas con MongoDB 7.0.5 o posterior. Si creaste una implementación local de Atlas con una versión anterior de MongoDB, no obtendrás la última versión de MongoDB automáticamente. Debes borrar las imágenes e implementaciones anteriores. Luego debes crear una nueva implementación de Atlas.

  • Descargue y cargue los datos de muestra.

    Ejemplo:

    1. Ejecute el siguiente comando para descargar los datos de muestra:

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

    2. Copia y pega el siguiente comando en la terminal y sustituye {port-number} por el puerto para la implementación:

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

Revisar las Limitaciones

Se aplican las siguientes limitaciones:

  • No se puede realizar una búsqueda simultánea.

  • No puedes exceder 1024 cláusulas booleanas.

Cree un índice de búsqueda de MongoDB y ejecute una query

Utiliza el comando atlas deployments para trabajar con MongoDB Search. Para obtener más información, consulta MongoDB Search.

Puede ejecutar este comando de las siguientes maneras:

  • Modo interactivo: el comando le solicita la configuración de MongoDB Search.

    Nota

    El modo interactivo te permite crear un índice de búsqueda básico de MongoDB con la configuración por defecto.

  • Modo no interactivo (banderas): se ejecuta el comando con las opciones especificadas. El comando no te solicita que proporciones valores adicionales.

  • Modo no interactivo (archivo de configuración): ejecutas el comando con los ajustes especificados en el archivo de configuración. El comando no le solicita que proporcione valores adicionales.

Se debe hacer clic en una de las siguientes pestañas para ver el comando de la moda preferida.

1

Crear un índice de búsqueda de MongoDB.

  1. Ejecuta el siguiente comando:

    atlas deployments search indexes create

  2. Especifique la implementación y pulse Enter.

  3. Especifique searchIndex como nombre del índice y pulse Enter.

  4. Especifique la base de datos sample_mflix y presione Enter.

  5. Especifica la colección movies y presiona Enter.

2

Conéctese a la implementación.

  1. Ejecuta el siguiente comando para conectarte a una implementación:

    atlas deployments connect

  2. Especifica la implementación a la que quieres conectarse y pulsa Enter.

  3. Especifica mongosh y pulsa Enter.

3

Cambie a la base de datos sample_mflix.

use sample_mflix
4

Ejecuta una query de ejemplo.

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

Crear un índice de búsqueda de MongoDB.

Copia y pega el siguiente comando en tu terminal y reemplaza {myRs} por tu implementación:

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

Conéctese a la implementación.

  1. Ejecuta el siguiente comando para conectarte a una implementación:

    atlas deployments connect

  2. Especifica la implementación a la que quieres conectarse y pulsa Enter.

  3. Especifica mongosh y pulsa Enter.

3

Cambie a la base de datos sample_mflix.

use sample_mflix
4

Ejecuta una query de ejemplo.

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

Crea un archivo de configuración indexDef.json.

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

Crear un índice de búsqueda de MongoDB.

  1. Ejecuta el siguiente comando:

    atlas deployments search indexes create --file indexDef.json

  2. Especifique la implementación y pulse Enter.

3

Conéctese a la implementación.

  1. Ejecuta el siguiente comando para conectarte a una implementación:

    atlas deployments connect

  2. Especifica la implementación a la que quieres conectarse y pulsa Enter.

  3. Especifica mongosh y pulsa Enter.

4

Cambie a la base de datos sample_mflix.

use sample_mflix
5

Ejecuta una query de ejemplo.

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'
  }
]

Utiliza MongoDB Vector Search con una implementación de Atlas

Use the atlas deployments command to work with MongoDB búsqueda vectorial. Para aprender más, consulta Cómo indexar incrustaciones vectoriales para la búsqueda vectorial.

Importante

Para utilizar la Atlas CLI con MongoDB Vector Search, debe crear una implementación de Atlas con MongoDB 7.0.5 o posterior. Si creaste una implementación local de Atlas con una versión anterior de MongoDB, no obtendrás la última versión de MongoDB automáticamente. Debes borrar las imágenes e implementaciones anteriores. Luego debes crear una nueva implementación de Atlas.

1

Crea un archivo de configuración indexDef-vector.json.

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

Crear un índice de búsqueda de MongoDB.

  1. Ejecuta el siguiente comando:

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

    Se debe especificar la ruta al archivo si el indexDef-vector.json no se encuentra en el directorio desde el que se desea ejecutar el comando.

  2. Especifique la implementación y pulse Enter.

3

Crea el archivo query-embeddings.js.

  1. Crea un archivo llamado query-embeddings.js para almacenar las incrustaciones de query.

  2. Copia y pega las siguientes incrustaciones para la string fantasy adventure en el archivo query-embeddings.js.

    QUERY_EMBEDDING = 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  1. Guarda el archivo query-embeddings.js.

4

Conéctese a la implementación.

  1. Ejecuta el siguiente comando para conectarte a una implementación:

    atlas deployments connect

  2. Especifica la implementación a la que quieres conectarse y pulsa Enter.

  3. Especifica mongosh y pulsa Enter.

5

Cambie a la base de datos sample_mflix.

use sample_mflix
6

Carga el archivo query-embeddings.js.

Para cargar el archivo, ejecute el siguiente comando, reemplazando <path-to-file> con la ruta completa a su archivo query-embeddings.js.

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

Puede verificar si el archivo se cargó correctamente ejecutando el siguiente comando:

QUERY_EMBEDDING.length
1536
7

Ejecuta una query de ejemplo.

db.embedded_movies.aggregate([
  {
    "$vectorSearch": {
      "index": "vectorSearchIndex",
      "path": "plot_embedding",
      "queryVector": QUERY_EMBEDDING,
      "numCandidates": 150,
      "limit": 10
    }
  },
  {
    "$project": {
      "_id": 0,
      "plot": 1,
      "title": 1,
      "score": { $meta: "vectorSearchScore" }
    }
  }
])
[
  {
    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',
    score: 0.9999986886978149
  },
  {
    plot: "To escape a war, a girl flees to a remote farmhouse and becomes part of an expansive family's unusual, perhaps even supernatural lifestyle.",
    title: 'Black Moon',
    score: 0.8282386660575867
  },
  {
    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
    title: 'Forest Warrior',
    score: 0.8196926712989807
  },
  {
    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
    title: 'Forest Warrior',
    score: 0.8196901082992554
  },
  {
    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
  },
  {
    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',
    score: 0.8112794160842896
  },
  {
    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
  }
]

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