Executar uma query no Atlas Vector Search

Nesta página

Visão geral
Dados de amostra
Realizar uma Vector Search
Criar um vetor binário BSON
Execute a query do Vector Search
Informações adicionais
Documentação da API

Visão geral

Neste guia, você aprenderá a usar o recurso Atlas Vector Search no driver Go usando o estágio do pipeline $vectorSearch. Esse estágio do pipeline permite realizar uma pesquisa semântica em seus documentos. Uma pesquisa semântica é um tipo de pesquisa que localiza informações com significado semelhante, mas não necessariamente idênticas, ao termo ou frase de pesquisa fornecida.

Importante

Compatibilidade de recursos

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

Dados de amostra

O exemplo nesta página query o campo plot_embedding da coleção embedded_movies, encontrado no banco de dados sample_mflix dos conjuntos de dados de amostra do Atlas.

O campo plot_embedding contém incorporações vetoriais com dimensões 1536, criadas usando o modelo de incorporação text-embedding-ada-002 do OpenAI.

Para saber como criar um cluster MongoDB Atlas gratuito e carregar os conjuntos de dados de amostra, consulte o guia Iniciar com 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 Atlas Search e Índices do Atlas Vector Search no guia Índices. Para saber mais sobre incorporações vetoriais, consulte Como indexar incorporações vetoriais para Vector Search na documentação do Atlas.

Depois de criar um índice de pesquisa vetorial nas incorporações vetoriais, você pode fazer referência a esse índice no agregação pipeline para executar a query de pesquisa vetorial.

As seções seguintes demonstram como criar um vetor binário BSON para seu vetor de consulta e como utilizar seu índice de pesquisa vetorial para executar uma query de pesquisa vetorial utilizando o campo plot_embedding .

Criar um vetor binário BSON

Neste exemplo, você pode criar um vetor dimensional 1536 para usar como vetor de query para sua query de pesquisa vetorial no campo plot_embedding . A consulta pesquisa o campo plot_embedding usando um vetor de incorporação para a string "viagem no tempo".

O exemplo a seguir mostra como converter essa incorporação de vetor em um vetor binário BSON que você pode usar como vetor de consulta:

// Converts a float32 array to a BSON vector
vectorFloat := bson.NewVector([]float32{-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 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// Converts the BSON vector to a BSON binary vector
queryVector := vectorFloat.Binary()

Se você precisar acessar uma fatia do vetor original, também poderá desserializar seu vetor de query de volta para um vetor BSON.

O exemplo seguinte mostra como converter o vetor de consulta de um vetor binário BSON para um vetor BSON utilizando o método NewVectorFromBinary():

// Converts the BSON binary vector back to a BSON vector
backToVectorFloat, err := bson.NewVectorFromBinary(queryVector)
if err != nil {
	log.Fatal(err)
}

Dica

Tipo de vetor de query

O exemplo anterior cria uma instância de um vetor binário BSON para servir como o vetor de consulta, mas você também pode usar uma array de valores BSON double. No entanto, recomendamos que você use um vetor binário BSON para melhorar a eficiência do armazenamento.

Execute a query do Vector Search

O exemplo abaixo mostra como construir um agregação pipeline que utiliza os métodos $vectorSearch e $project para executar uma pesquisa vetorial de vizinho mais próximo (ANN) com as seguintes especificações:

Executa a query do campo plot_embedding com o binário BSON queryVector
Define o número de vizinhos mais próximos utilizados na pesquisa para 150 utilizando a opção numCandidates
Utiliza o índice vector_search criado no campo plot_embedding
Retorna 5 documentos com os campos plot, title e score especificados

// Creates the aggregation pipeline
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "vector_index"},
		{"path", "plot_embedding"},
		{"queryVector", queryVector},
		{"numCandidates", 150},
		{"limit", 5},
	}}}
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"plot", 1},
		{"title", 1},
		{"score", bson.D{{"$meta", "vectorSearchScore"}}},
	}}}
// Runs the aggregation pipeline
cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
if err != nil {
	log.Fatalf("failed to retrieve data from the server: %v", err)
}
// Displays the results
type ProjectedMovieResult struct {
	Title string  `bson:"title"`
	Plot  string  `bson:"plot"`
	Score float64 `bson:"score"`
}
var results []ProjectedMovieResult
if err = cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score)
}

Title: Thrill Seekers
Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.
Score: 0.92730712890625
Title: About Time
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.
Score: 0.926605224609375
Title: The Time Machine
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.
Score: 0.9239959716796875
Title: Timecop
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.
Score: 0.923583984375
Title: Crusade in Jeans
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...
Score: 0.9222412109375

Informações adicionais

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

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

Documentação da API

Para saber mais sobre qualquer um dos métodos ou tipos discutidos neste guia, consulte a seguinte documentação da API:

Voltar

Modificar execução CRUD

Agregação