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Driver C#/ .NET

Executar uma query no Atlas Vector Search

Visão geral

Você pode usar o Atlas Vector Search para realizar pesquisa vetorial em seus dados armazenados no Atlas. A pesquisa vetorial permite que você query seus dados com base no significado semântico, em vez de apenas em correspondências de palavras-chave, o que ajuda a recuperar resultados de pesquisa mais relevantes. Ele permite que seus aplicativos com tecnologia de IA ofereçam suporte a casos de uso como pesquisa semântica, pesquisa híbrida e pesquisa generativa, incluindo geração aumentada de recuperação (RAG).

Ao usar o Atlas como um banco de dados vetorial, você pode indexar perfeitamente dados vetoriais junto com seus outros dados no Atlas. Isso permite que você filtre campos em sua coleção e execute queries de pesquisa vetorial em dados vetoriais. Você também pode combinar a pesquisa vetorial com queries de pesquisa de texto completo para obter os resultados mais relevantes para o seu caso de uso. Você pode integrar o Atlas Vector Search com estruturas e serviços de IA populares para implementar facilmente a pesquisa vetorial em seus aplicativos.

Para saber mais sobre o Atlas Vector Search, consulte o guia do Atlas Vector Search na documentação do MongoDB Atlas.

Dados de amostra

Os exemplos neste guia usam a coleção sample_mflix.embedded_movies no banco de dados sample_mflix. Para obter o conjunto de dados de amostra para esta coleção, consulte Introdução ao uso do driver .NET/C#.

Os exemplos neste guia usam a seguinte classe de amostra para modelar documentos na coleção sample_mflix.embedded_movies:

[BsonIgnoreExtraElements]
public class EmbeddedMovie
{
    public ObjectId Id { get; set; }
    public string Plot { get; set; }
    public string Title { get; set; }
    [BsonElement("plot_embedding")]
    public float[] PlotEmbedding { get; set; }
}

Tipos de incorporação de vetor compatíveis

As incorporações de vetores são vetores que você usa para representar seus dados. Essas incorporações capturam relacionamentos significativos em seus dados e permitem tarefas como pesquisa e recuperação semântica.

O driver .NET/C# oferece suporte a incorporações de vetores de vários tipos. As seções a seguir descrevem os tipos de incorporações vetoriais suportadas.

Representações de array

O driver .NET/C# oferece suporte às seguintes representações do tipo array em embeddings vetoriais:

  • BsonArray

  • Memory

  • ReadOnlyMemory

  • float[] e a double[]

O exemplo a seguir mostra uma classe com propriedades dos tipos anteriores:

public class BsonArrayVectors
{
    public BsonArray BsonArrayVector { get; set; }
    public Memory<float> MemoryVector { get; set; }
    public ReadOnlyMemory<float> ReadOnlyMemoryVector { get; set; }
    public float[] FloatArrayVector { get; set; }
}

Dica

Para saber mais sobre como usar os tipos Memory e ReadOnlyMemory, consulte a seção Melhorar o desempenho da serialização de arrays do guia de serialização.

Representações de Vetores Binários

O driver .NET/C# oferece suporte às seguintes representações vetoriais binárias em incorporações de vetor:

  • BinaryVectorFloat32 (não suportado em arquiteturas big-endian)

  • BinaryVectorInt8

  • BinaryVectorPackedBit

  • Memory<float>, Memory<byte> , Memory<sbyte>

  • ReadOnlyMemory<float>, ReadOnlyMemory<byte> , ReadOnlyMemory<sbyte>

  • float[], byte[] , sbyte[]

Observação

Você deve usar o atributo BinaryVector ao especificar representações de vetores binários dos tipos Memory<T>, ReadOnlyMemory<T> ou array.

O exemplo a seguir mostra uma classe com propriedades dos tipos anteriores:

public class BinaryVectors
{
    public BinaryVectorInt8 ValuesInt8 { get; set; }
    public BinaryVectorPackedBit ValuesPackedBit { get; set; }
    public BinaryVectorFloat32 ValuesFloat { get; set; }
    [BinaryVector(BinaryVectorDataType.Int8)]
    public Memory<byte> ValuesByte { get; set; }
    [BinaryVector(BinaryVectorDataType.Float32)]
    public float[] ValuesFloat { get; set; }
}

Serialização de Dados Vetoriais Binários

Você pode serializar dados do tipo vetor binário Int8 como byte ou sbyte. Você também pode serializar dados do tipo vetor binário Float32 como float. O exemplo a seguir serializa dados de vetor binário Int8 e Float32:

[BinaryVector(BinaryVectorDataType.Int8)]
public Memory<byte> ValuesByte { get; set; }
[BinaryVector(BinaryVectorDataType.Int8)]
public Memory<sbyte> ValuesSByte { get; set; }
[BinaryVector(BinaryVectorDataType.Float32)]
public float[] ValuesFloat { get; set; }

Você pode desserializar dados de vetor PackedBit para um vetor binário representado tipo de dados byte somente se os dados do vetor tiverem um valor de preenchimento de 0. Se os dados do vetor tiverem um valor de preenchimento não igual a 0, você poderá desserializá-los somente para um BsonVectorPackedBit.

Exemplo de consulta de pesquisa vetorial

Você pode realizar uma pesquisa vetorial chamando o método VectorSearch(). Para realizar uma pesquisa vetorial em uma coleção, é necessário primeiro ter uma coleção com um campo que contenha dados vetoriais e um índice de pesquisa vetorial que abranja esse campo.

Para saber mais sobre como configurar uma coleção para pesquisa vetorial, consulte o guia Atlas Vector Search na documentação do MongoDB Atlas.

Você pode converter dados BinaryVectorFloat32, BinaryVectorInt8 e BinaryVectorPackedBit para o tipo BsonBinaryData para usar em uma consulta de pesquisa vetorial usando o método ToQueryVector(). O exemplo a seguir converte BinaryVectorInt8 em um objeto BsonBinaryData:

var binaryVector = new BinaryVectorInt8(new sbyte[] { 0, 1, 2, 3, 4 });
var queryVector = binaryVector.ToQueryVector();

Você pode especificar seus dados vetoriais representados como array como uma instância da classe QueryVector para usar em uma query de pesquisa vetorial. O exemplo a seguir cria uma array de valores ReadOnlyMemory<float> como um objeto QueryVector para usar em uma query de pesquisa vetorial:

QueryVector v = new QueryVector(new ReadOnlyMemory<float>([1.2f, 2.3f]));

Considere a coleção embedded_movies no banco de dados sample_mflix. Você pode usar uma fase $vectorSearch para executar uma pesquisa semântica no campo plot_embedding dos documentos na coleção. As seções a seguir descrevem diferentes métodos para realizar operações de Atlas Vector Search nesta coleção.

Dica

Para obter o conjunto de dados de amostra usado no exemplo a seguir, consulte Introdução ao uso do driver .NET/C#. Para criar o índice de amostra do Atlas Vector Search usado no exemplo a seguir, consulte Criar um índice do Atlas Vector Search no manual do Atlas.

Exemplo de pipeline de agregação

Este exemplo executa as seguintes etapas para executar uma Atlas Vector Search query em uma coleção que contém dados vetoriais e um índice de pesquisa vetorial no campo PlotEmbedding:

  1. Cria um array que contém os dados vetoriais representados em forma de array para pesquisa

  2. Especifica um objeto VectorSearchOptions que contém o nome do índice e o número de vizinhos mais próximos a serem utilizados durante a pesquisa

  3. Cria um pipeline de agregação que utiliza o estágio VectorSearch() para realizar a query de pesquisa vetorial e um estágio Project() para mostrar apenas os campos Title, Plot e Score

  4. Imprime os resultados da query

// Defines vector embeddings for the string "time travel"
var vector = new[] 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// Specifies that the vector search will consider the 150 nearest neighbors
// in the specified index
var options = new VectorSearchOptions<EmbeddedMovie>()
{
   IndexName = "vector_index",
   NumberOfCandidates = 150
};
// Builds aggregation pipeline and specifies that the $vectorSearch stage
// returns 10 results
var pipeline = new EmptyPipelineDefinition<EmbeddedMovie>()
   .VectorSearch(m => m.PlotEmbedding, vector, 10, options)
   .Project(Builders<EmbeddedMovie>.Projection
      .Include(m => m.Title)
      .Include(m => m.Plot)
      .MetaVectorSearchScore(m => m.Score);

Os resultados do exemplo anterior contêm os seguintes documentos:

{ "_id" : { "$oid" : "573a13a0f29313caabd04a4f" }, "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : 0.926971435546875 }
{ "_id" : { "$oid" : "573a13d8f29313caabda6557" }, "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", "score" : 0. 9267120361328125 }
{ "_id" : { "$oid" : "573a1399f29313caabceec0e" }, "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : 0.9235687255859375 }
{ "_id" : { "$oid" : "573a13a5f29313caabd13b4b" }, "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", "score" : 0.9228668212890625 }
{ "_id" : { "$oid" : "573a13aef29313caabd2e2d7" }, "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : 0.9228515625 }
{ "_id" : { "$oid" : "573a1399f29313caabcee36f" }, "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.", "score" : 0.9199066162109375 }
{ "_id" : { "$oid" : "573a13c6f29313caabd715d3" }, "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : 0.919403076171875 }
{ "_id" : { "$oid" : "573a13d4f29313caabd98c13" }, "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland", "score" : 0.9191131591796875 }
{ "_id" : { "$oid" : "573a13b6f29313caabd477fa" }, "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : 0. 917755126953125 }
{ "_id" : { "$oid" : "573a13b3f29313caabd3ebd4" }, "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", "score" : 0.9172210693359375 }

Exemplo LINQ

O exemplo de código a seguir executa a mesma query de pesquisa vetorial que o exemplo anterior, mas utiliza a sintaxe LINQ em vez da sintaxe do pipeline de agregação:

var results = collection.AsQueryable()
    .VectorSearch(m => m.PlotEmbedding, vector, 10, options)
    .Select(m => new { m.Title, m.Plot });

Informações adicionais

Para aprender mais sobre o Atlas Vector Search, consulte a guia Atlas Vector Search na documentação do MongoDB Atlas.

Documentação da API

Para saber mais sobre qualquer uma das funções ou tipos discutidos neste guia, consulte a seguinte documentação da API:

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