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Ejecuta una query de búsqueda vectorial en MongoDB

Overview

En esta guía, puedes aprender cómo usar la funcionalidad de MongoDB Vector Search en el driver de Node.js.

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

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

Importante

Compatibilidad de características

Para aprender qué versiones de MongoDB Atlas admiten esta funcionalidad, consulte Limitaciones en la documentación de MongoDB Atlas.

Realiza una búsqueda vectorial

Para usar esta funcionalidad, debes crear un índice de búsqueda vectorial e indexar tus incrustaciones vectoriales. Para aprender cómo crear programáticamente un índice de búsqueda vectorial, consulta la La sección de MongoDB Search y MongoDB Vector Search Indexes en la guía de índices. Para aprender más sobre incrustaciones vectoriales, consulta Cómo crear incrustaciones vectoriales en la documentación de Atlas.

Después de crear un índice de búsqueda vectorial en tus incrustaciones vectoriales, puedes referenciar este índice en tu etapa de pipeline, como se muestra en el siguiente ejemplo.

Datos de muestra

El ejemplo de esta página muestra cómo construir un pipeline de agregación que usa el $vectorSearch etapa para realizar una búsqueda vectorial en la colección sample_mflix.embedded_movies en los conjuntos de datos de muestra Atlas.

Para aprender cómo crear un clúster gratuito de MongoDB Atlas y cargar los conjuntos de datos de ejemplo, consulta la guía MongoDB Get Started.

Ejemplo de búsqueda vectorial

Puedes realizar una consulta de búsqueda vectorial utilizando la etapa $vectorSearch en una pipeline de agregación. Para realizar una búsqueda vectorial en una colección, primero debes tener una colección con un campo que contenga incrustaciones vectoriales y un índice de búsqueda vectorial que cubra ese campo.

En el siguiente ejemplo, el pipeline de agregación busca en el campo plot de cada documento de la colección texto semánticamente relacionado con el término "viaje en el tiempo". El campo queryVector de la pipeline $vectorSearch es la representación vectorial de su query.

const { MongoClient } = require("mongodb");
// Replace with the connection string to your Atlas cluster to connect
const uri = "<connection-string>";
const client = new MongoClient(uri);
async function run() {
    try {
        await client.connect();
        // Sets the namespace
        const database = client.db("sample_mflix");
        const coll = database.collection("embedded_movies");
        // Defines the pipeline
        const agg = [
            {
              '$vectorSearch': {
                'index': 'vector_index',
                'path': 'plot_embedding',
                'queryVector': Binary.fromFloat32Array(Float32Array.from([
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                ])),
                'numCandidates': 150,
                'limit': 10
              }
            }, {
              '$project': {
                '_id': 0,
                'plot': 1,
                'title': 1,
                'score': {
                  '$meta': 'vectorSearchScore'
                }
              }
            }
          ];
        // Runs pipeline
        const result = coll.aggregate(agg);
        // Prints results
        for await (const doc of result) {
            console.log(doc);
        }
    }   finally {
            await client.close();
    }
}
run().catch(console.dir);
{
   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: 0.9259490966796875
}
{
   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: 0.9253997802734375
}
{
   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: 0.922332763671875
}
{
   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: 0.92205810546875
}
{
   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: 0.921875
}
{
   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: 0.9202728271484375
}
{
   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: 0.9198150634765625
}
{
   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: 0.91961669921875
}
{
   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: 0.9174346923828125
}
{
   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: 0.9165191650390625
}

Este query utiliza la etapa $vectorSearch para:

  • Realizar una búsqueda vectorial de ANN (ANN)

  • Buscar el término especificado en el campo plot_embedding

  • Establece la cantidad de vecinos más cercanos utilizados en la búsqueda en 150 usando la opción numCandidates

  • Devolver un máximo de 10 documentos de la consulta usando la opción limit

Utiliza la etapa $project para:

  • Incluir solo los campos plot y title de la película en los resultados

  • Agregar un campo score para mostrar la relevancia de cada resultado con respecto al término de búsqueda

Información Adicional

Para ver más tutoriales de MongoDB Vector Search para el driver de Node.js, consulta los tutoriales de MongoDB Vector Search en la documentación de Atlas.

Para obtener más información sobre la sintaxis de la etapa $vectorSearch del pipeline, consulte las secciones Sintaxis y Campos de la guía Crear y ejecutar consultas en la sección MongoDB Vector Search de la documentación de Atlas.

Documentación de la API

Para obtener más información sobre el método aggregate(), consulta aggregate() en la documentación de la API.

Volver

MongoDB búsqueda

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