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Utilice Atlas Search y Atlas Vector Search con una implementación de Atlas

Este tutorial le muestra cómo utilizar el atlas deployments Comando para crear un índice de búsqueda Atlas, ejecutar una consulta y trabajar con Atlas 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 obtener más información sobre las instrucciones de instalación de Atlas CLI para otros sistemas operativos, consulte Instalar o actualizar Atlas CLI.

  • Instalar versiónmongosh 2.0 o posterior.

    Ejemplo (MacOS):

    brew install mongosh

    Para obtener más información sobre las mongosh instrucciones de instalación para otros sistemas operativos, consulte Instalar mongosh.

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

    Ejemplo (MacOS):

    brew install mongodb-compass

    Para obtener más información sobre las instrucciones de instalación de Compass para otros sistemas operativos, consulte Descargar e instalar Compass.

  • Instalar herramientas de base de datos MongoDB.

    Ejemplo (MacOS):

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

    Para obtener más información sobre las instrucciones de instalación de las herramientas de base de datos MongoDB para otros sistemas operativos, consulte Instalación de las herramientas de base de datos.

  • Instalar 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 RHEL de Linux.

  • Crear una implementación de Atlas.

    Ejemplo:

    atlas deployments setup

    Para obtener más información, consulte Crear una implementación de Atlas local.

    Importante

    Para utilizar el Atlas CLI con Atlas Vector Search, debes crear una implementación de Atlas con MongoDB 7.0.5 o posterior. Si has creado una implementación local de Atlas con una versión anterior de MongoDB, no obtendrás automáticamente la versión más reciente de MongoDB. 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 es posible realizar búsquedas concurrentes.

  • No se pueden exceder 1024 cláusulas booleanas.

Crear un índice de búsqueda de Atlas y ejecutar una consulta

Utilice el atlas deployments comando para trabajar con la Búsqueda en Atlas. Para obtener más información,consulte Búsqueda en Atlas.

Puede ejecutar este comando de las siguientes maneras:

  • Modo interactivo: el comando le solicita la configuración de búsqueda de Atlas.

    Nota

    El modo interactivo le permite crear un índice de búsqueda de Atlas básico con configuraciones predeterminadas.

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

  • Modo no interactivo (archivo de configuración): se ejecuta el comando con la configuración especificada en el archivo de configuración. El comando no solicita 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 Atlas.

  1. Ejecuta el siguiente comando:

    atlas deployments search indexes create

  2. Especifique el despliegue y presione Enter.

  3. Especifique searchIndex como nombre para el índice y presione Enter.

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

  5. Especifique la colección movies y presione Enter.

2

Conectarse a la implementación.

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

    atlas deployments connect

  2. Especifique la implementación a la que desea conectarse y presione Enter.

  3. Especifique mongosh y presione Enter.

3

Cambiar a la sample_mflix base de datos.

use sample_mflix
4

Ejecute una consulta 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 Atlas.

Copie y pegue el siguiente comando en su terminal y reemplace {myRs} con su implementación:

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

Conectarse a la implementación.

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

    atlas deployments connect

  2. Especifique la implementación a la que desea conectarse y presione Enter.

  3. Especifique mongosh y presione Enter.

3

Cambiar a la sample_mflix base de datos.

use sample_mflix
4

Ejecute una consulta 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

Cree un indexDef.json archivo de configuración.

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

Crear un índice de búsqueda Atlas.

  1. Ejecuta el siguiente comando:

    atlas deployments search indexes create --file indexDef.json

  2. Especifique la implementación y presione Enter.

3

Conectarse a la implementación.

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

    atlas deployments connect

  2. Especifique la implementación a la que desea conectarse y presione Enter.

  3. Especifique mongosh y presione Enter.

4

Cambiar a la sample_mflix base de datos.

use sample_mflix
5

Ejecute una consulta 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'
  }
]

Utilice la búsqueda vectorial de Atlas con una implementación de Atlas

Usa el comando atlas deployments para trabajar con Atlas Vector Search. Para obtener más información, consulta Cómo indexar incrustaciones vectoriales para la búsqueda vectorial.

Importante

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

1

Cree un indexDef-vector.json archivo de configuración.

{
  "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 Atlas.

  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 presione Enter.

3

Crea el query-embeddings.js archivo.

  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

Conectarse a la implementación.

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

    atlas deployments connect

  2. Especifique la implementación a la que desea conectarse y presione Enter.

  3. Especifique mongosh y presione Enter.

5

Cambiar a la sample_mflix base de datos.

use sample_mflix
6

Cargue el query-embeddings.js archivo.

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

Ejecute una consulta 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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