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Ejecuta una query de búsqueda vectorial en MongoDB

Overview

Puede usar MongoDB Vector Search para realizar búsquedas vectoriales en sus datos almacenados en Atlas. Esta búsqueda le permite consultar sus datos basándose en el significado semántico, en lugar de solo en coincidencias de palabras clave, lo que le ayuda a obtener resultados de búsqueda más relevantes. Permite que sus aplicaciones basadas en IA admitan casos de uso como la búsqueda semántica, la búsqueda híbrida y la búsqueda generativa, incluyendo la Generación Aumentada por Recuperación (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, consulte Guía debúsqueda de vectores de MongoDB 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 incrustación de vectores admitidos

Lasincrustaciones vectoriales son vectores que se utilizan para representar los datos. Estas incrustaciones capturan relaciones significativas en los 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 controlador .NET/C# admite las siguientes representaciones del tipo de matriz en incrustaciones vectoriales:

  • 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 big-endian)

  • BinaryVectorInt8

  • BinaryVectorPackedBit

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

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

  • float[], byte[], sbyte[]

Nota

Debe utilizar el atributo BinaryVector al especificar representaciones vectoriales binarias de los tipos Memory<T>, ReadOnlyMemory<T> o matriz.

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 datos de vector binario Int8 como byte o sbyte. También puede serializar datos 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; }

Solo se pueden deserializar PackedBit datos vectoriales a un byte tipo de datos representado por un vector binario si el valor de relleno 0 es. Si el valor de relleno es distinto 0 de, solo se puede deserializar BsonVectorPackedBit a.

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.

Puede convertir datos BinaryVectorFloat32, BinaryVectorInt8 y BinaryVectorPackedBit al tipo BsonBinaryData para usarlos en una consulta de búsqueda vectorial mediante 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();

Puede especificar sus datos vectoriales representados por matriz como una instancia de la QueryVector clase para usar en una consulta de búsqueda vectorial. El siguiente ejemplo crea una matriz de ReadOnlyMemory<float> valores como un QueryVector objeto para usar en una consulta de búsqueda vectorial:

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

Considere la colección embedded_movies en la base de datos sample_mflix. 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 búsqueda vectorial de MongoDB en esta colección.

Tip

Para obtener el conjunto de datos de ejemplo utilizado en el siguiente ejemplo, consulte Introducción al controlador .NET/C#. Para crear el índice de búsqueda vectorial de MongoDB de ejemplo utilizado en el siguiente ejemplo, consulte 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 consulta de búsqueda vectorial de MongoDB 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 a utilizar 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 búsquedas de texto consultando un índice de búsqueda vectorial de MongoDB con incrustación automática. Para obtener más información sobre cómo crear un índice con incrustaciones automáticas, consulte Búsqueda de MongoDB e Índices de búsqueda vectorial de MongoDB.

El siguiente ejemplo construye la misma consulta de búsqueda vectorial que el ejemplo anterior, pero en su lugar utiliza un índice de búsqueda vectorial MongoDB con incrustación automática en el campo plot denominado 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

Al utilizar un índice de incrustación automática, usted proporciona directamente el texto que se buscará en lugar de una representación vectorial de ese texto.

Para obtener más información sobre la incrustación automática de índices de búsqueda vectorial de MongoDB, consulte la sección Modelo de índice de búsqueda vectorial de MongoDB de la guía Búsqueda de MongoDB e índices de búsqueda vectorial de MongoDB.

Información Adicional

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

Documentación de la API

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

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