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Vector Search

Use visualizações com a Vector Search do MongoDB

Você pode criar um índice do MongoDB Vector Search em uma visualização para transformar documentos e collections de forma que você possa indexar parcialmente uma collection, oferecer suporte a tipos ou modelos de dados incompatíveis e muito mais.

Os exemplos a seguir usam o banco de dados de amostra sample_mflix.

Observação

Desambiguação

Esta página explora visualizações padrão. Para saber mais sobre visualizações materializadas sob demanda, consulte Visualizações materializadas sob demanda.

Para aprender sobre as diferenças entre os tipos de visualização, consulte Comparação com visualizações materializadas sob demanda.

Requisitos

Você deve usar o MongoDB 8.0 ou superior.

Para editar uma visualização, você deve ter uma função de Usuário administrador e usar o comando de banco de dados collMod.

Limitações

Exemplo: filtrar documentos

Para criar uma visualização, você deve ter o privilégio createCollection.

Você pode filtrar documentos para criar índices parciais em uma coleção. O exemplo a seguir cria uma View na coleção sample_mflix.embedded_movies para que apenas os documentos que possuam um campo de incorporação de vetor sejam indexados.

➤ Use o menu descartar Selecione seu linguagem para definir a interface dos exemplo nesta página.

1

Conecte-se ao seu cluster usando o mongosh.

Para saber mais, consulte Conectar-se a um cluster via mongosh.

2

Mudar para o banco de dados sample_mflix.

use sample_mflix
3

Criar uma visualização moviesWithEmbeddings.

db.createView(
  "moviesWithEmbeddings",
  "embedded_movies",
  [
    {
      "$match": {
        "$expr": {
          "$ne": [
            {
              "$type": "$plot_embedding_voyage_3_large"
            },
            "missing"
          ]
        }
      }
    }
  ]
)
4

No Atlas, VáGo para a Clusters página do seu projeto.

  1. Se ainda não tiver sido exibido, selecione a organização que contém seu projeto no menu Organizations na barra de navegação.

  2. Se ainda não estiver exibido, selecione o projeto desejado no menu Projects na barra de navegação.

  3. Na barra lateral, clique em Clusters sob o título Database.

A página Clusters é exibida.

5

No Atlas, vá Search & Vector Search para a página para seu cluster.

Você pode acessar a página de pesquisa do MongoDB a partir da opção Search & Vector Search ou do Data Explorer.

  1. Se ainda não tiver sido exibido, selecione a organização que contém seu projeto no menu Organizations na barra de navegação.

  2. Se ainda não estiver exibido, selecione seu projeto no menu Projects na barra de navegação.

  3. Na barra lateral, clique em Search & Vector Search sob o título Database.

    Se você não tiver clusters, clique em Create cluster para criar um. Para saber mais, consulte Criar um cluster.

  4. Se o seu projeto tiver vários clusters, selecione o cluster que deseja usar no menu suspenso Select cluster e clique em Go to Atlas Search.

    A página Search & Vector Search é exibida.

  1. Se ainda não tiver sido exibido, selecione a organização que contém seu projeto no menu Organizations na barra de navegação.

  2. Se ainda não estiver exibido, selecione seu projeto no menu Projects na barra de navegação.

  3. Na barra lateral, clique em Data Explorer sob o título Database.

  4. Expanda o banco de dados e selecione a coleção.

  5. Clique na guia Indexes da coleção.

    A página Atlas Search é exibida.

6

Clique em Create Search Index.

7

Inicie a configuração do seu índice.

Faça as seguintes seleções na página e clique em Next.

Search Type

Selecione o tipo de índice Atlas Vector Search.

Index Name and Data Source

Especifique as seguintes informações:

  • 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.

IMPORTANTE:

Seu índice do MongoDB Search é denominado default por padrão. Se você manter esse nome, seu índice será o índice de pesquisa padrão para qualquer consulta do MongoDB Search que não especifique uma opção index diferente em seus operadores. Se você estiver criando vários índices, recomendamos que mantenha uma convenção de nomenclatura descritiva consistente em seus índices.

8

Especifique uma definição de índice.

  1. Especifique a seguinte definição de índice:

    {
      "fields": [
        {
          "type": "vector",
          "numDimensions": 2048,
          "path": "plot_embedding_voyage_3_large",
          "similarity": "cosine"
        }
      ]
    }

  2. Clique em Next.

9

Clique em Create Vector Search Index.

O Atlas exibe uma janela modal para que você saiba que seu índice está crescendo.

10

Feche a janela modal You're All Set! clicando no botão Close.

11

Verifique o status.

O índice recém-criado é exibido na aba Atlas Search. Enquanto o índice está construindo, o campo Status lê Build in Progress. Quando o índice terminar de construir, o campo Status lê Active.

Observação

Collections maiores demoram mais tempo para indexar. Você receberá uma notificação por e-mail quando seu índice terminar a criação.

12

Executar uma consulta no índice parcial embeddingsIndex.

Observação

O exemplo a seguir consulta o índice embeddingsIndex executando o comando .aggregate na visualização chamada moviesWithEmbeddings. Se o seu cluster está executando o MongoDB v8.0, você deve consultar a coleção de origem (por exemplo, embedded_movies) usando o índice na visualização. Faça upgrade para o MongoDB v8.1+ para consultar a visualização diretamente.

  1. Salve as seguintes incorporações em um arquivo chamado query-embeddings.js:

    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    Este arquivo contém as incorporações para o termo de query time travel.

  2. Em mongosh, carregue o arquivo em mongosh para usar as incorporações na sua query:

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

  3. Execute a seguinte query:

    1use sample_mflix
    2db.moviesWithEmbeddings.aggregate([
    3  {
    4    "$vectorSearch": {
    5      "index": "embeddingsIndex",
    6      "path": "plot_embedding_voyage_3_large",
    7      "queryVector": TIME_TRAVEL_EMBEDDING,
    8      "numCandidates": 100,
    9      "limit": 10
    10    }
    11  },
    12  {
    13    "$project": {
    14      "_id": 0,
    15      "plot": 1,
    16      "title": 1,
    17    }
    18  }
    19])
    [
      {
        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 psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
        title: 'Retroactive'
      },
      {
        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: '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: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.',
        title: 'Back to the Future Part II'
      },
      {
        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: '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: '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: '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 modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.',
        title: 'The Final Countdown'
      }
    ]
1

Conecte‑se à sua implantação MongoDB usando mongosh.

Para saber mais, consulte Conectar-se a um cluster via mongosh.

2

Mudar para o banco de dados sample_mflix.

use sample_mflix
3

Criar uma visualização moviesWithEmbeddings.

db.createView(
  "moviesWithEmbeddings",
  "embedded_movies",
  [
    {
      "$match": {
        "$expr": {
          "$ne": [
            {
              "$type": "$plot_embedding_voyage_3_large"
            },
            "missing"
          ]
        }
      }
    }
  ]
)
4

Crie um índice do MongoDB Vector Search na visualização.

db.moviesWithEmbeddings.createSearchIndex(
  "embeddingsIndex",
  "vectorSearch",
  {
    "fields": [
      {
        "type": "vector",
        "numDimensions": 2048,
        "path": "plot_embedding_voyage_3_large",
        "similarity": "cosine"
      }
    ]
  }
)
5

Executar uma consulta no índice parcial embeddingsIndex.

Observação

O exemplo a seguir consulta o índice embeddingsIndex executando o comando .aggregate na visualização chamada moviesWithEmbeddings. Se o seu cluster está executando o MongoDB v8.0, você deve consultar a coleção de origem (por exemplo, embedded_movies) usando o índice na visualização. Faça upgrade para o MongoDB v8.1+ para consultar a visualização diretamente.

  1. Salve as seguintes incorporações em um arquivo chamado query-embeddings.js:

    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    Este arquivo contém as incorporações para o termo de query time travel.

  2. Em mongosh, carregue o arquivo em mongosh para usar as incorporações na sua query:

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

  3. Execute a seguinte query:

    1use sample_mflix
    2db.moviesWithEmbeddings.aggregate([
    3  {
    4    "$vectorSearch": {
    5      "index": "embeddingsIndex",
    6      "path": "plot_embedding_voyage_3_large",
    7      "queryVector": TIME_TRAVEL_EMBEDDING,
    8      "numCandidates": 100,
    9      "limit": 10
    10    }
    11  },
    12  {
    13    "$project": {
    14      "_id": 0,
    15      "plot": 1,
    16      "title": 1,
    17    }
    18  }
    19])
    [
      {
        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 psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
        title: 'Retroactive'
      },
      {
        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: '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: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.',
        title: 'Back to the Future Part II'
      },
      {
        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: '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: '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: '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 modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.',
        title: 'The Final Countdown'
      }
    ]

Considerações de desempenho

Transformações de exibição altamente complexas podem levar a um desempenho mais lento quando o Atlas lê a exibição para filtrar e transformar a collection de origem. Nesse cenário, considere a criação de uma visualização materializada para evitar carga de replicação adicional no Atlas. Para evitar a latência de query causada pela transformação de exibição, você pode fazer a query na collection de origem diretamente para recuperar os documentos originais.

Solução de problemas

Índices mudam para FAILED

Os índices mudam para o status FAILED nos seguintes cenários:

  • Você cria um índice em uma visualização incompatível com o MongoDB Vector Search.

  • Você edita uma visualização de uma forma que não atende aos requisitos de compatibilidade da Vector Search do MongoDB .

  • Você remove ou altera a coleção de origem de uma visualização.

    Por exemplo, se uma visualização for criada sobre outra visualização e você alterar a fonte da visualização pai para outra coleção.

    Observação

    Essa limitação também se aplica se uma visualização for descendente de outras visualizações. Por exemplo, você não pode alterar ou remover a coleção de origem da qual todos os descendentes se originam.

Índices mudam para STALE

Os índices mudam para o status STALE nos seguintes cenários:

Aviso

Se o pipeline de agregação definido na sua visualização for incompatível com os documentos da coleção, a replicação de pesquisa falhará. Por exemplo, se uma expressão $toDouble atuar em um campo de documento que contém uma array, a replicação falha. Certifique-se de que sua visualização funcione com todos os documentos na coleção sem erros.

  • Se a definição da visualização causar uma falha de agregação enquanto um índice for READY, o índice se tornará STALE. O índice retornará para READY depois que você resolver o documento ou alterar a definição de visualização para que ela não falhe mais. Quando STALE, o índice permanece consultável. Se o índice cair do oplog, uma reconstrução do índice será acionada.

  • Se a definição de visualização causar uma falha no pipeline de agregação enquanto o índice estiver BUILDING, a construção do índice ficará travada até que você corrija o documento. O índice retornará para READY após você resolver o documento ou modificar a definição de exibição para que não falhe mais.

Você pode visualizar os status do índice na IU do Atlas na página de detalhes do status do índice.

Erro: $search é válido apenas como o primeiro estágio de um pipeline

Esse erro aparece quando você faz query de uma visualização usando uma versão do MongoDB anterior a 8.1.

  • Se você usa uma versão do MongoDB anterior a 8.0, recomendamos que atualize para 8.1+ para consultar a visualização diretamente. Você pode atualizar para 8.0 para consultar a coleção de origem.

  • Se você usar o MongoDB 8.0, deverá consultar o índice de visualização em relação à collection de origem. Por exemplo, execute .aggregate() na collection em vez de na visualização.

Processo de índice

Quando você cria um índice do MongoDB Vector Search em uma visualização, o processo mongot executa as mesmas tarefas de quando você cria um índice do MongoDB Vector Search em uma collection regular. O processo do mongot:

  1. Cria índices de Vector Search MongoDB com base nas regras na definição de índice para a coleção.

  2. Monitora change streams para o estado atual dos documentos e índices para as collections para as quais você definiu os índices da MongoDB Vector Search .

  3. Processa as queries do MongoDB Vector Search e retorna os IDs dos documento e outros metadados da pesquisa dos documentos correspondentes para mongod, que então faz uma pesquisa completa do documento e retorna os resultados ao cliente.

Quando você cria um índice do MongoDB Vector Search em uma visualização, a definição de visualização é aplicada durante as etapas 1 e 2, e os documentos transformados são armazenados no índice do MongoDB Vector Search no disco.

Saiba mais

Para saber mais sobre visualizações, consulte Visualizações.

Para criar um índice do MongoDB Search em uma visualização, consulte Usar visualizações com o MongoDB Search.

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