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Ejecuta una query de búsqueda vectorial en MongoDB

Overview

Puedes utilizar la Búsqueda Vectorial de MongoDB para realizar una búsqueda vectorial en tus datos almacenados en Atlas. La búsqueda vectorial te permite query tus datos basándote en el significado semántico en lugar de solo coincidencias de palabras clave, lo que ayuda a recuperar resultados de búsqueda más relevantes. Permite que tus aplicaciones impulsadas por IA admitan casos de uso como la búsqueda semántica, la búsqueda híbrida y la búsqueda generativa, incluida la generación de recuperación aumentada (RAG).

Al usar Atlas como base de datos vectorial, puedes indexar sin problemas datos vectoriales junto con tus otros datos en Atlas. Esto te permite filtrar campos en tu colección y realizar consultas de búsqueda vectorial de datos vectoriales. También puedes combinar la búsqueda vectorial con consultas de búsqueda de texto completo para devolver los resultados más relevantes para tu caso de uso. Puedes integrar MongoDB Vector Search con los principales frameworks y servicios de IA para implementar fácilmente la búsqueda vectorial en tus aplicaciones.

Para obtener más información sobre MongoDB Vector Search, consulta la MongoDB Vector Search Guía en la documentación de MongoDB Atlas.

Datos de muestra

Los ejemplos de esta guía utilizan el sample_mflix.embedded_movies Colección en la base de datos sample_mflix. Para obtener el conjunto de datos de muestra de esta colección, consulte Introducción al controlador .NET/C#.

Los ejemplos de esta guía utilizan la siguiente clase de muestra para modelar documentos en la colección 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 incrustaciones vectoriales compatibles

Las incrustaciones vectoriales son vectores que se utilizan para representar tus datos. Estas incrustaciones capturan relaciones significativas en tus datos y permiten tareas como la búsqueda y recuperación semántica.

El controlador .NET/C# admite incrustaciones vectoriales de varios tipos. Las siguientes secciones describen los tipos de incrustaciones vectoriales admitidos.

Representaciones de matrices

El Driver .NET/C# admite las siguientes representaciones del tipo de arreglo en los vectores de embedding:

  • BsonArray

  • Memory

  • ReadOnlyMemory

  • float[] y double[]

El siguiente ejemplo muestra una clase con propiedades de los 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; }
}

Tip

Para obtener más información sobre el uso de los Memory ReadOnlyMemory tipos y, consulte la sección Mejorar el rendimiento de la serialización de matrices de la guía de serialización.

Representaciones Vectoriales Binarias

El controlador .NET/C# admite las siguientes representaciones vectoriales binarias en incrustaciones vectoriales:

  • BinaryVectorFloat32 (no compatible con arquitecturas de tipo "big-endian")

  • BinaryVectorInt8

  • BinaryVectorPackedBit

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

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

  • float[], byte[], sbyte[]

Nota

Debe usar el atributo BinaryVector cuando especifique representaciones vectoriales binarias de los tipos Memory<T>, ReadOnlyMemory<T> o de arreglo.

El siguiente ejemplo muestra una clase con propiedades de los 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; }
}

Serialización de datos vectoriales binarios

Puede serializar los datos tipados de vector binario Int8 como byte o sbyte. También puedes serializar datos del tipo de vector binario Float32 como float. El siguiente ejemplo serializa datos de vector binario Int8 y 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; }

Puedes deserializar PackedBit datos de vector a un vector binario representado byte tipo de datos únicamente si los datos del vector tienen un valor de relleno de 0. Si los datos vectoriales tienen un valor de relleno que no es igual a 0, solo puedes deserializarlos a un BsonVectorPackedBit.

Ejemplo de consulta de búsqueda vectorial

Puede realizar una consulta de búsqueda vectorial llamando al método VectorSearch(). Para realizar una búsqueda vectorial en una colección, primero debe tener una colección con un campo que contenga datos vectoriales y un índice de búsqueda vectorial que cubra dicho campo.

Para obtener más información sobre cómo configurar una colección para la búsqueda vectorial, consulte la guía de búsqueda vectorial de MongoDB en la documentación de MongoDB Atlas.

Puedes convertir los datos BinaryVectorFloat32, BinaryVectorInt8 y BinaryVectorPackedBit al tipo BsonBinaryData para usarlos en una búsqueda vectorial usando el método ToQueryVector(). El siguiente ejemplo convierte BinaryVectorInt8 en un objeto BsonBinaryData:

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

Puedes especificar tus datos vectoriales representados como arreglo como una instancia de la clase QueryVector para usar en una query de búsqueda vectorial. El siguiente ejemplo crea un arreglo de ReadOnlyMemory<float> valores como un objeto QueryVector para usar en una query de búsqueda vectorial:

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

Considera la colección embedded_movies en la base de datos sample_mflix. Se puede usar una etapa $vectorSearch para realizar una búsqueda semántica en el campo plot_embedding de los documentos de la colección. Las siguientes secciones describen diferentes métodos para realizar operaciones de MongoDB Vector Search en esta colección.

Tip

Para obtener el conjunto de datos de muestra utilizado en el siguiente ejemplo, consulta Empezar con el Driver .NET/C#. Para crear el índice de búsqueda vectorial de MongoDB de muestra utilizado en el siguiente ejemplo, consulta Crear un índice de búsqueda vectorial de MongoDB en el manual de Atlas.

Ejemplo de pipeline de agregación

Este ejemplo realiza los siguientes pasos para ejecutar una MongoDB Vector Search query en una colección que contiene datos vectoriales y un índice de búsqueda vectorial en el campo PlotEmbedding:

  1. Crea una matriz que contiene los datos vectoriales representados por la matriz para buscar

  2. Especifica un objeto VectorSearchOptions que contiene el nombre del índice y el número de vecinos más cercanos que se usarán durante la búsqueda

  3. Crea una canalización de agregación que utiliza la etapa VectorSearch() para realizar la consulta de búsqueda vectorial y una etapa Project() para mostrar solo los campos Title, Plot y Score

  4. Imprime los resultados de la 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);

Los resultados del ejemplo anterior contienen los siguientes 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 }

Ejemplo de LINQ

El siguiente ejemplo de código realiza la misma consulta de búsqueda vectorial que el ejemplo anterior, pero utiliza la sintaxis LINQ en lugar de la sintaxis de canalización de agregación:

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

Consultar un índice de incrustación automática

Puede automatizar la generación de vectores para búsqueda de texto consultando un índice de MongoDB Vector Search de incrustación automática. Para aprender a crear un índice de incrustaciones automatizadas, consulte MongoDB Search y MongoDB Vector Search.

El siguiente ejemplo construye la misma consulta de vector de búsqueda que el ejemplo anterior, pero en su lugar utiliza un índice de MongoDB Vector Search con auto-embeddings en el campo plot llamado auto_embedding_index:

var options = new VectorSearchOptions<EmbeddedMovie>()
{
    IndexName = "auto_embedding_index",
    NumberOfCandidates = 150
};
var query = collection.Aggregate()
    .VectorSearch(m => m.Plot, "time travel", 10, options)
    .Project(Builders<EmbeddedMovie>.Projection
        .Include(m => m.Title)
        .Include(m => m.Plot));

Nota

Cuando se utiliza un índice de incrustación automática, se proporciona directamente el texto a buscar y no una representación vectorial de ese texto.

Para más información sobre la inserción automática de índices de MongoDB Vector Search, consulta la sección Modelo de índice de MongoDB Vector Search de la guía de MongoDB Search e índices de MongoDB Vector Search.

Información Adicional

Para obtener más información sobre MongoDB Vector Search, consulta la MongoDB Vector Search guía en la documentación de MongoDB Atlas.

Documentación de la API

Para obtener más información sobre cualquiera de las funciones o tipos tratados en esta guía, consulta la siguiente documentación de la API:

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