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Executar uma query de pesquisa vetorial do MongoDB

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 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 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 MongoDB Search e Índices de Vector Search do MongoDB. 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)
}

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)
}

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
Próximo
Monitoramento
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