Executar uma query de pesquisa vetorial do MongoDB

A incorporação automatizada está disponível como um recurso de visualização somente para o MongoDB Community Edition v8.2 e versões mais recentes. O recurso e a documentação correspondente podem mudar a qualquer momento durante o período de Pré-visualização. Para saber mais, consulte recursos de visualização.

Visão geral

Neste guia, você pode aprender a usar o recurso MongoDB Vector Search no driver Go usando o estágio de pipeline $vectorSearch. Essa etapa do pipeline permite que você execute uma pesquisa semântica nos seus documentos. Pesquisa semântica é um tipo de pesquisa que localiza informações que são semelhantes em significado, mas não necessariamente idênticas ao termo ou frase de pesquisa fornecido.

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 consulta o campo plot_embedding da coleção embedded_movies, localizada no banco de dados sample_mflix dos conjuntos de dados de exemplo 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 aprender como criar um cluster gratuito do MongoDB Atlas e carregar os conjuntos de dados de amostra, consulte Introdução ao Atlas.

Executar uma query de pesquisa vetorial do MongoDB

Para utilizar este recurso, você deve criar um índice de pesquisa vetorial e indexar suas incorporações de vetor. Para saber mais sobre como criar programaticamente um índice de pesquisa vetorial, consulte Índices de pesquisa do MongoDB e do MongoDB Vector Search. Para saber mais sobre incorporações vetoriais, consulte Como indexar incorporações vetoriais para pesquisa vetorial 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

Consultar um índice de incorporação automática

Você pode automatizar a geração de vetores para pesquisa vetorial executando a query em um índice de auto-embedding do MongoDB Vector Search. Para saber como criar um índice de incorporação automática usando o driver Go, consulte a seção Modelo de índice de pesquisa de incorporação automática do MongoDB do guia Índices.

O exemplo a seguir cria um pipeline de agregação que pesquisa similaridade semântica com a frase "time travel" no campo plot. A query utiliza um índice MongoDB Vector Search de auto-embedding no campo plot denominado auto_embedding_index:

// Creates the $vectorSearch stage that queries the
// auto-embedding index named "auto_embedding_index"
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "auto_embedding_index"},
		{"path", "plot"},
		{"query", bson.D{{"text", "time travel"}}},
		{"model", "voyage-4"},
		{"numCandidates", 150},
		{"limit", 10},
	}},
}
// Projects only the title and plot fields
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"title", 1},
		{"plot", 1},
	}},
}
// 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)
}
defer cursor.Close(ctx)
type ProjectedMovieResult struct {
	Title string `bson:"title"`
	Plot  string `bson:"plot"`
}
var results []ProjectedMovieResult
if err := cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v\nPlot: %v\n---\n\n", result.Title, result.Plot)
}

Title: Manuel on the Island of Wonders
Plot: Manuel's fantasy travel through Time goes from Long Ago (Episode 1 - O jardim proibido / Le Jardin interdit) through Now (Episode 2 - O pique-nique dos sonhos / Le Pique-nique des rèves), ...
---
Title: 11 Minutes Ago
Plot: Traveling in 11-minute increments, a time-tumbler from 48-years in the future spends two years of his life weaving through a two-hour wedding reception.
---
Title: Time Freak
Plot: A neurotic inventor creates a time machine and gets lost traveling around yesterday.
---
Title: Timecrimes
Plot: A man accidentally gets into a time machine and travels back in time nearly an hour. Finding himself will be the first of a series of disasters of unforeseeable consequences.
---
Title: The Little Girl Who Conquered Time
Plot: A high-school girl acquires the ability to time travel.
---
Title: The Little Girl Who Conquered Time
Plot: A high-school girl acquires the ability to time travel.
---
Title: Time Traveller
Plot: A high-school girl acquires the ability to time travel.
---
Title: Je t'aime je t'aime
Plot: Recovering from an attempted suicide, a man is selected to participate in a time travel experiment that has only been tested on mice. A malfunction in the experiment causes the man to ...
---
Title: A.P.E.X.
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: The Ah of Life
Plot: Theoretical mathematician, Nigel Kline finds himself the subject of his own vertical time study. Entering into Einstein's relativity, three versions of Nigel face off with each other, weaving time and space in a world of fluid moments.
---
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.
---

Observação

Quando você executar query de um índice de auto-embedding do MongoDB pesquisa vetorial, forneça o texto para pesquisar no campo query (por exemplo {"text", "time travel"}) e um nome de modelo compatível no campo model. Os modelos suportados são voyage-4-lite, voyage-4, voyage-4-large e voyage-code-3. Você não precisa calcular ou passar um valor queryVector .

Informações adicionais

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

Para saber mais sobre a sintaxe do estágio do pipeline $vectorSearch, consulte as seções "Sintaxe e campos" do guia Crie e execute queries na seção "MongoDB 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

MongoDB Search

Monitoramento