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Executar uma query de Vector Search do MongoDB

Visão geral

Neste guia, você pode aprender como usar o recurso MongoDB Vector Search no driver Node.js

Você pode utilizar a Vector Search do MongoDB para realizar uma pesquisa de vetor em seus dados armazenados no Atlas. A pesquisa vetorial permite consultar seus dados com base no significado semântica, em vez de apenas nas correspondências de palavras-chave, o que ajuda a recuperar resultados de pesquisa mais relevantes. Ele permite que seus aplicativos baseados em IA ofereçam suporte a casos de uso, como pesquisa semântica, pesquisa híbrida e pesquisa generativa, incluindo a Geração Aumentada de Recuperação (RAG).

Para saber mais sobre a Vector Search do MongoDB , consulte os guias deVector Search do MongoDB na documentação do MongoDB Atlas .

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

Para utilizar este recurso, você deve criar um índice de pesquisa de vetor e indexar suas incorporações de vetor. Para saber mais sobre como criar programaticamente um índice de pesquisa vetorial, consulte a seção Índices do MongoDB Search e do MongoDB Vector Search no guia Índices. Para saber mais sobre incorporações vetoriais, consulte Como criar incorporações vetoriais na documentação do Atlas .

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

Para ver mais tutoriais de Vector Search do MongoDB para o driver Node.js , consulte os tutoriais de Vector Search do MongoDB na documentação do Atlas .

Para saber mais sobre a sintaxe do $vectorSearch estágio de pipeline do, consulte as seções Sintaxe e Campos do guia Criar e Executar Queries na seção MongoDB Vector Search da documentação do Atlas .

Documentação da API

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

Voltar

MongoDB Search

Próximo

Monitoramento

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