Executar uma query de Vector Search do MongoDB

Visão geral

In this guide, you can learn how to use the MongoDB Vector Search feature in the Node.js driver.

You can use MongoDB Vector Search to perform a vector search on your data stored in Atlas. Vector search allows you to query your data based on semantic meaning rather than just keyword matches, which helps you retrieve more relevant search results. It enables your AI-powered applications to support use cases such as semantic search, hybrid search, and generative search, including Retrieval-Augmented Generation (RAG).

To learn more about MongoDB Vector Search, see the MongoDB Vector Search guides in the MongoDB Atlas documentation.

Importante

Compatibilidade de recursos

Para saber quais versões do MongoDB Atlas suportam este recurso, consulte Limitações na documentação do MongoDB Atlas.

Realizar uma Vector Search

To use this feature, you must create a vector search index and index your vector embeddings. To learn about how to programmatically create a vector search index, see the MongoDB Search and MongoDB Vector Search Indexes section in the Indexes guide. To learn more about vector embeddings, see How to Create Vector Embeddings in the Atlas documentation.

Após criar um índice de pesquisa vetorial em suas incorporações vetoriais, você pode referenciar esse índice na etapa do pipeline, conforme mostrado no exemplo a seguir.

Dados de amostra

O exemplo nesta página mostra como criar um pipeline de agregação que usa o estágio $vectorSearch para executar uma pesquisa vetorial na coleção sample_mflix.embedded_movies nos conjuntos de dados de amostra do Atlas.

Para saber como criar um cluster MongoDB Atlas gratuito e carregar os conjuntos de dados de amostra, consulte o guia Iniciar com Atlas .

Exemplo de Vector Search

Você pode realizar uma query de pesquisa vetorial usando o estágio $vectorSearch em um pipeline de agregação. Para realizar uma pesquisa vetorial em uma coleção, é necessário primeiro ter uma coleção com um campo que contenha incorporações de vetor e um índice de pesquisa vetorial que cubra esse campo.

No exemplo a seguir, o pipeline de agregação pesquisa o campo plot de cada documento na coleção por texto semanticamente relacionado ao termo "viagem no tempo". O campo queryVector no pipeline $vectorSearch é a representação vetorial da sua consulta.

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 = [
            {
              '$vectorSearch': {
                'index': 'vector_index',
                'path': 'plot_embedding',
                'queryVector': Binary.fromFloat32Array(Float32Array.from([
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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136
                ])),
                'numCandidates': 150,
                'limit': 10
              }
            }, {
              '$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
}

Esta query usa a fase $vectorSearch para:

Realizar uma pesquisa de vetor de vizinho mais próximo aproximado (ANN)
Pesquise o termo especificado no campo plot_embedding.
Defina o número de vizinhos mais próximos utilizados na pesquisa para 150 através da opção numCandidates
Retorne no máximo 10 documentos da consulta usando a opção limit

Ele usa o estágio $project para:

Inclua apenas os campos de filme plot e title nos resultados
Adicione um campo score para mostrar a relevância de cada resultado para o termo de pesquisa

Informações adicionais

To see more MongoDB Vector Search tutorials for the Node.js driver, see the MongoDB Vector Search tutorials in the Atlas documentation.

To learn more about the syntax of the $vectorSearch pipeline stage, see the Syntax and Fields sections of the Create and Run Queries guide in the MongoDB Vector Search section of the Atlas documentation.

Documentação da API

Para saber mais sobre o método aggregate(), consulte aggregate() na documentação da API.

Voltar

MongoDB Search

Monitoramento