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Executar queries no Vector Search

As queries do MongoDB Vector Search assumem a forma de um estágio de pipeline de agregação. Para as queries do$vectorSearch, MongoDB Vector Search retorna os resultados da sua pesquisa semântica.

Observação

O MongoDB Vector Search suporta ANN pesquisa em clusters que executam o MongoDB v6.0.11, v7.0.2, ou posterior e pesquisa ENN em clusters que executam o MongoDB v6.0.16, v7.0.10, v7.3.2, ou mais tarde.

O estágio $vectorSearch executa uma pesquisa de ANN ou ENN em um vetor no campo especificado.

Para a pesquisa de ANN, o MongoDB Vector Search encontra incorporações vetoriais em seus dados que estão mais próximas da incorporação do vetor em sua consulta com base em sua proximidade no espaço multidimensional e com base no número de vizinhos que ele considera. Ele usa o algoritmo Hierarchical Navigable Small Worlds e encontra as incorporações vetoriais mais semelhantes à incorporação vetorial em sua query sem examinar todos os vetores. Portanto, a pesquisa ANN é ideal para consultar grandes conjuntos de dados sem filtragem significativa.

Observação

Normalmente, considera-se que a recuperação ideal para a pesquisa ANN esteja em torno de 90a95% de sobreposição nos resultados com a pesquisa ENN, mas com latência significativamente menor. Isso fornece um bom equilíbrio entre precisão e desempenho. Para conseguir isso com a Vector Search do MongoDB, ajuste o parâmetro numCandidates no momento da query.

Para a pesquisa ENN, o MongoDB Vector Search pesquisa exaustivamente todas as incorporações de vetor indexadas calculando a distância entre todas as incorporações e encontra o vizinho mais próximo exato para a incorporação do vetor em sua consulta. Isso é computacionalmente intensivo e pode impacto negativamente a latência da query. Portanto, recomendamos pesquisas ENN para os seguintes casos de uso:

  • Você deseja determinar o recall e a precisão da query ANN usando os resultados ideais e exatos para a query ENN.

  • Você deseja executar queries de menos de documentos 10000 sem precisar ajustar o número de vizinhos mais próximos a serem considerados.

  • Você deseja incluir pré-filtros seletivos em sua query em relação a coleções em que menos de 5% dos seus dados atendem ao pré-filtro fornecido.

Se você ativar a quantização automática, o MongoDB Vector Search usará somente os vetores de fidelidade total para consultas ENN.

O campo que você deseja pesquisar deve ser indexado como tipo de vetor MongoDB Vector Search vector dentro de um tipo de índice vectorSearch.

$vectorSearch

Um pipeline $vectorSearch tem a seguinte forma de protótipo:

{
"$vectorSearch": {
"exact": true | false,
"filter": {<filter-specification>},
"index": "<index-name>",
"limit": <number-of-results>,
"numCandidates": <number-of-candidates>,
"path": "<field-to-search>",
"queryVector": [<array-of-numbers>]
}
}

O estágio $vectorSearch recebe um documento com os seguintes campos:

Campo
Tipo
necessidade
Descrição

exact

booleano

Opcional

Isso é necessário se numCandidates for omitido.

Sinalizador que especifica se a pesquisa ENN ou ANN deve ser executada. O valor pode ser um dos seguintes:

  • false - para executar a pesquisa ANN

  • true - para executar a pesquisa ENN

Se omitido, o padrão é false.

O MongoDB Vector Search suporta pesquisa ANN em clusters que executam o MongoDB v6.0.11, v7.0.2, ou posterior e pesquisa ENN em clusters que executam o MongoDB v6.0.16, v7.0.10, v7.3.2, ou mais tarde.

filter

documento

Opcional

Expressão MQL que compara um campo indexado para ser usada como pré-filtro. Você pode aplicar filtro a valores booleanos, data, objectId, valores numéricos, string e UUID, incluindo arrays desses tipos.

Para saber quais operadores MQL o MongoDB Vector Search suporta em seu filtro, consulte Pré-filtro de Vector Search do MongoDB.

index

string

Obrigatório

Nome do índice do MongoDB Vector Search a ser usado.

A Vector Search do MongoDB não retorna resultados se você digitar incorretamente o nome do índice ou se o índice especificado ainda não existir no cluster.

limit

número

Obrigatório

Número (somente do tipo int) de documentos para retornar nos resultados. Este valor não pode exceder o valor de numCandidates se você especificar numCandidates.

numCandidates

número

Opcional

Este campo é obrigatório se exact for false ou omitido.

Número de vizinhos mais próximos a serem utilizados durante a pesquisa. O valor deve ser menor ou igual a (<=) 10000. Você não pode especificar um número menor que o número de documentos a serem retornados (limit).

Recomendamos especificar um número pelo menos 20 vezes maior que o número de documentos a serem retornados (limit) para aumentar a precisão.

Esse padrão de solicitação excessiva é a maneira recomendada de equilibrar a latência e recall em suas pesquisas ANN, e recomendamos ajustar esse parâmetro com base no tamanho específico do seu conjunto de dados e nos requisitos de query.

Para aprender mais sobre outras variáveis que podem impactar este parâmetro, consulte Seleção de numCandidates.

path

string

Obrigatório

Campo do tipo vetor indexado para pesquisa.

queryVector

array de números

Obrigatório

Array de números de float32, vetores BSON BinData com subtipo float32, ou vetores BSON BinData com subtipo int1 ou tipo int8 que representam o vetor de consulta.

Para aprender mais sobre como gerar vetores BSON binData com subtipo float32, int8, int1, consulte Como ingerir vetores pré-quantizados.

O tamanho da array deve corresponder ao número do vetor dimensions especificado na definição de índice para o campo.

Você deve incorporar sua query com o mesmo modelo que utilizou para incorporar os dados.

Você pode executar query de suas incorporações com vetores de fidelidade total, desde que o subtipo de vetor seja o mesmo. Isso só é possível com vetores binData com subtipo float32. Se você usar qualquer outro subtipo (int8 ou int1), o MongoDB Vector Search não retornará nenhum resultado ou erro.

Você pode executar$vectorSearchconsultas usando a interface do usuário do Atlas , mongoshe qualquer driver MongoDB .

Você também pode utilizar a Vector Search do MongoDB com implementações locais do Atlas que você cria com o Atlas CLI. Para saber mais, consulte Criar uma implantação de Atlas local.

$vectorSearch é suportado apenas em clusters Atlas que executam as seguintes versões MongoDB:

  • v6.0.11

  • v7.0.2 e posterior (incluindo RCs).

$vectorSearch não pode ser usado na definição de exibição e nos seguintes estágios de pipeline:

Você pode passar os resultados de $vectorSearch para esse estágio.

$vectorSearch deve ser o primeiro estágio de qualquer pipeline onde aparece.

Você deve indexar os campos a serem pesquisados usando o $vectorSearch estágio dentro de uma definição de índice do tipo vectorSearch. Você pode indexar os seguintes tipos de campos em uma definição de índice do tipo vectorSearch do MongoDB Vector Search :

  • Campos que contêm incorporações vetoriais como tipo de vetor.

  • Campos adicionais, como o tipo de filtro para viabilizar a pesquisa vetorial em dados pré-filtrados.

Para saber mais sobre esses tipos de campo do MongoDB Vector Search, consulte Como indexar campos para o Vector Search.

O MongoDB Vector Search atribui uma pontuação, em um intervalo fixo de 0 a 1 (onde 0 indica baixa similaridade e 1 indica alta similaridade), a cada documento retornado.

A pontuação é calculada de acordo com a medida de similaridade que você especifica na definição do índice. Para semelhanças entre cosine e dotProduct, o MongoDB Vector Search normaliza a pontuação para garantir que a pontuação não seja negativa.

Para cosine, o MongoDB Vector Search utiliza o seguinte algoritmo para normalizar a pontuação:

score = (1 + cosine(v1,v2)) / 2

Este algoritmo normaliza a pontuação considerando a pontuação de similaridade do vetor de documento (v1) e do vetor de query (v2), que tem o intervalo [-1, 1]. O MongoDB Vector Search adiciona 1 à pontuação de similaridade para normalizar a pontuação para um intervalo [0, 2] e depois divide por 2 para garantir um valor entre 0 e 1.

Para dotProduct, o MongoDB Vector Search utiliza o seguinte algoritmo para normalizar a pontuação:

score = (1 + dotProduct(v1,v2)) / 2

Este algoritmo normaliza a pontuação considerando a pontuação de similaridade do vetor de documento (v1) e do vetor de query (v2), que tem o intervalo [-1, 1]. O MongoDB Vector Search adiciona 1 à pontuação de similaridade para normalizar a pontuação para um intervalo [0, 2] e depois divide por 2 para garantir um valor entre 0 e 1.

Para euclidean similaridade, o MongoDB Vector Search usa o seguinte algoritmo para normalizar a pontuação e garantir um valor entre 0 e 1:

score = 1 / (1 + euclidean(v1,v2))

Este algoritmo normaliza a pontuação calculando a distância euclideana, que é a distância entre o vetor de documento (v1) e o vetor de query (v2), que tem o intervalo [0, ]. O MongoDB Vector Search transforma a distância em uma pontuação de similaridade adicionando 1 à distância e, em seguida, divide 1 pelo resultado para garantir um valor entre 0 e 1.

Cada documento retornado inclui a pontuação como metadados. Para incluir a pontuação de cada documento retornado junto com o conjunto de resultados, use um estágio $project em seu pipeline de agregação.

Para recuperar a pontuação dos resultados da sua consulta de Vector Search do MongoDB, use o vectorSearchScore como o valor na expressão $meta. Ou seja, após o estágio $vectorSearch, no estágio $project o campo score recebe a expressão $meta. A expressão exige o valor vectorSearchScore para retornar a pontuação dos documentos para a pesquisa vetorial.

Exemplo

1db.<collection>.aggregate([
2 {
3 "$vectorSearch": {
4 <query-syntax>
5 }
6 },
7 {
8 "$project": {
9 "<field-to-include>": 1,
10 "<field-to-exclude>": 0,
11 "score": { "$meta": "vectorSearchScore" }
12 }
13 }
14])

Observação

A pré-filtragem de seus dados não afeta a pontuação que o MongoDB Vector Search retorna usando $vectorSearchScore para $vectorSearch consultas.

A opção filter $vectorSearch corresponde aos valores booleanos, data, objectId, valores numéricos, de string e UUID do BSON, incluindo arrays desses tipos.

Você deve indexar os campos nos quais deseja filtrar os dados como tipo de filtro em uma definição de índice do tipo vectorSearch. É útil filtrar os dados para restringir o escopo da pesquisa semântica e garantir que nem todos os vetores sejam considerados para comparação.

O MongoDB Vector Search suporta a opção $vectorSearch filter para os seguintes operadores MQL:

Tipo
Operador MQL

Iguais

faixa

No conjunto

Lógica

O MongoDB Vector Search suporta a forma abreviada de $eq. Na forma abreviada, você não precisa especificar $eq na query.

Exemplo

Por exemplo, considere o seguinte filtro com $eq:

"filter": { "_id": { "$eq": ObjectId("5a9427648b0beebeb69537a5") }

Você pode executar a consulta anterior usando a forma curta de $eq da seguinte maneira:

"filter": { "_id": ObjectId("5a9427648b0beebeb69537a5") }

Você também pode especificar uma array de filtros em uma única query usando o operador $and.

Exemplo

Por exemplo, considere o seguinte pré-filtro para documentos com um campo genres igual a Action e um campo year com o valor 1999, 2000 ou 2001:

"filter": {
"$and": [
{ "genres": "Action" },
{ "year": { "$in": [ 1999, 2000, 2001 ] } }
]
}

Para a pesquisa ANN, recomendamos especificar um número numCandidates pelo menos 20 vezes maior do que o número de documentos a serem retornados (limit) para aumentar a precisão. Por exemplo, se você definir limit para retornar 5 resultados, considere definir numCandidates como 100 como ponto de partida. Um número mais alto também reduz quaisquer discrepâncias entre os resultados da sua consulta ANN e ENN. Para aprender mais, veja Como medir a precisão dos resultados da sua query.

Este padrão de solicitação excessiva é a maneira recomendada de compensar a latência e o recall em suas pesquisas ANN. No entanto, recomendamos ajustar este parâmetro com base no tamanho específico do seu conjunto de dados e nos requisitos da query. Para garantir que você obtenha resultados precisos, considere as seguintes variáveis:

  • Tamanho do índice: coleções maiores normalmente exigem valores de numCandidates mais altos para manter o recall. Uma coleção com milhões de vetores pode precisar de muito mais candidatos do que uma com milhares de vetores.

  • Valor-limite: como numCandidates está altamente correlacionado com o tamanho do índice, valores baixos de limit exigem valores proporcionalmente mais altos de numCandidates para manter o recall.

  • Quantização de vetores: vetores quantizados reduzem o armazenamento ao custo da precisão. O uso de vetores quantizados (subtipos int8 ou int1) pode exigir valores de numCandidates mais altos em comparação com vetores de precisão total float32 para manter um recall semelhante.

Recomendamos nós de pesquisa dedicados para isolar o processamento de query de pesquisa vetorial. Você poderá ver o desempenho aprimorado da consulta nos nós de pesquisa dedicados. Observe que os sistemas de alta CPU podem fornecer mais melhorias de desempenho. Quando o MongoDB Vector Search é executado em nós de pesquisa, o MongoDB Vector Search paraleliza a execução da query em segmentos de dados.

A paralelização do processamento de queries melhora o tempo de resposta em muitos casos, como queries em grandes conjuntos de dados. O uso do paralelismo intraquery durante o processamento de query do MongoDB Vector Search utiliza mais recursos, mas melhora a latência para cada query individual.

Observação

A Vector Search do MongoDB não garante que cada query seja executada simultaneamente. Por exemplo, quando muitas queries simultâneas são enfileiradas, o MongoDB Vector Search pode voltar para a execução de thread único.

Você pode ver resultados inconsistentes para as mesmas queries sucessivas. Para atenuar isso, aumente o valor de numCandidates nas queries $vectorSearch.

As queries a seguir pesquisam a coleção de amostras de queries sample_mflix.embedded_movies usando o estágio $vectorSearch. As queries pesquisam o campo plot_embedding_voyage_3_large, que contém incorporações criadas usando o modelo de incorporações voyage-3-large da Voyage AI.


➤ Use o menu suspenso Selecione a linguagem para definir a linguagem dos exemplos nesta página.


Antes de executar estes exemplos, realize as seguintes ações:

Observação

Se você usar mongosh, colar o queryVector do código de amostra no seu terminal pode levar algum tempo, dependendo da sua máquina.

A query abaixo usa o estágio $vectorSearch para pesquisar o campo plot_embedding_voyage_3_large usando incorporações vetoriais para a string viagem no tempo. Ela considera até 150 vizinhos mais próximos, e retorna 10 documentos nos resultados. A query também especifica um estágio $project para fazer o seguinte:

  • Excluir o campo _id e inclua somente os campos plot e title nos resultados.

  • Adicione um campo chamado score que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.

  1. Salve as seguintes incorporações em um arquivo chamado query-embeddings.js:

    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  2. Carregue o arquivo em mongosh para usar as incorporações na sua query:

    load(/<path-to-file>/query-embeddings.js');
  3. Execute a seguinte query:

    1db.embedded_movies.aggregate([
    2 {
    3 "$vectorSearch": {
    4 "index": "vector_index",
    5 "path": "plot_embedding_voyage_3_large",
    6 "queryVector": TIME_TRAVEL_EMBEDDING,
    7 "numCandidates": 150,
    8 "limit": 10,
    9 "quantization": "scalar"
    10 }
    11 },
    12 {
    13 "$project": {
    14 "_id": 0,
    15 "plot": 1,
    16 "title": 1,
    17 "score": { $meta: "vectorSearchScore" }
    18 }
    19 }
    20])
    1[
    2 {
    3 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.',
    4 title: 'About Time',
    5 score: 0.7710106372833252
    6 },
    7 {
    8 plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
    9 title: 'Retroactive',
    10 score: 0.760047972202301
    11 },
    12 {
    13 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...',
    14 title: 'A.P.E.X.',
    15 score: 0.7576861381530762
    16 },
    17 {
    18 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.',
    19 title: 'Timecop',
    20 score: 0.7576561570167542
    21 },
    22 {
    23 plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.',
    24 title: 'Back to the Future Part II',
    25 score: 0.7521393895149231
    26 },
    27 {
    28 plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
    29 title: 'Thrill Seekers',
    30 score: 0.7509932518005371
    31 },
    32 {
    33 plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...',
    34 title: 'Timerider: The Adventure of Lyle Swann',
    35 score: 0.7502642869949341
    36 },
    37 {
    38 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.',
    39 title: 'The Time Machine',
    40 score: 0.7502503395080566
    41 },
    42 {
    43 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.',
    44 title: "The Time Traveler's Wife",
    45 score: 0.749496340751648
    46 },
    47 {
    48 plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.',
    49 title: 'The Final Countdown',
    50 score: 0.7469133734703064
    51 }
    52]

A query a seguir filtra os documentos para filmes lançados entre 01 de janeiro de 1955 e 01 de janeiro de 1975 antes de executar o Atlas Search semântica em relação aos dados vetoriais de amostra. Utiliza o operador $and para executar uma operação lógica AND das datas especificadas. Em seguida, ele pesquisa o campo plot_embedding_voyage_3_large nos documentos filtrados para 150 vizinhos mais próximos usando as incorporações vetoriais para a string playground e retorna 10 documentos nos resultados. A consulta também especifica um estágio $project para fazer o seguinte:

  • Exclua o campo _id e inclua apenas os campos plot, title e year nos resultados.

  • Adicione um campo denominado score que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.

  1. Salve as seguintes incorporações em um arquivo chamado query-embeddings.js:

    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  2. Carregue o arquivo em mongosh para usar as incorporações na sua query:

    load('/<path-to-file>/query-embeddings.js');
  3. Execute a seguinte query:

    1db.embedded_movies.aggregate([
    2 {
    3 "$vectorSearch": {
    4 "index": "vector_index",
    5 "path": "plot_embedding_voyage_3_large",
    6 "filter": {
    7 "$and": [
    8 {
    9 "year": { "$gt": 1955 }
    10 },
    11 {
    12 "year": { "$lt": 1975 }
    13 }
    14 ]
    15 },
    16 "queryVector": KIDS_ADVENTURE_EMBEDDING,
    17 "numCandidates": 150,
    18 "limit": 10
    19 }
    20 },
    21 {
    22 "$project": {
    23 "_id": 0,
    24 "title": 1,
    25 "plot": 1,
    26 "year": 1,
    27 "score": { $meta: "vectorSearchScore" }
    28 }
    29 }
    30])
    1[
    2 {
    3 plot: 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.',
    4 title: 'Chitty Chitty Bang Bang',
    5 year: 1968,
    6 score: 0.7489712834358215
    7 },
    8 {
    9 plot: 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.',
    10 title: 'The Little Prince',
    11 year: 1974,
    12 score: 0.7346078753471375
    13 },
    14 {
    15 plot: 'In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.',
    16 title: 'In Search of the Castaways',
    17 year: 1962,
    18 score: 0.7281590104103088
    19 },
    20 {
    21 plot: 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.',
    22 title: 'Bedknobs and Broomsticks',
    23 year: 1971,
    24 score: 0.720349133014679
    25 },
    26 {
    27 plot: "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.",
    28 title: 'Pastoral Hide and Seek',
    29 year: 1974,
    30 score: 0.7195663452148438
    31 },
    32 {
    33 plot: 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...',
    34 title: 'Peter Pan',
    35 year: 1960,
    36 score: 0.7195032238960266
    37 },
    38 {
    39 plot: 'When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ...',
    40 title: 'The Cowboys',
    41 year: 1972,
    42 score: 0.717952311038971
    43 },
    44 {
    45 plot: 'When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war.',
    46 title: 'The 7th Voyage of Sinbad',
    47 year: 1958,
    48 score: 0.7148033380508423
    49 },
    50 {
    51 plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.',
    52 title: 'The Red Balloon',
    53 year: 1956,
    54 score: 0.7147064805030823
    55 },
    56 {
    57 plot: 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...',
    58 title: 'Perri',
    59 year: 1957,
    60 score: 0.712087094783783
    61 }
    62]

O MongoDB Vector Search filtra os documentos com base no valor do campo year, que varia entre 1955 e 1975. Ela retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.

Dica

Exemplos adicionais de filtro

O tutorial Como realizar uma pesquisa semântica em dados em seu cluster Atlas demonstra outros pré-filtros em queries de pesquisa semântica em relação aos dados incorporados na coleção sample_mflix.embedded_movies.

A query abaixo usa o estágio $vectorSearch para pesquisar o campo plot_embedding_voyage_3_large usando incorporações vetoriais para a string viagem no tempo. Ela considera até 150 vizinhos mais próximos, e retorna 10 documentos nos resultados. A query também especifica um estágio $project para fazer o seguinte:

  • Excluir o campo _id e inclua somente os campos plot e title nos resultados.

  • Adicione um campo chamado score que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.

1using MongoDB.Bson;
2using MongoDB.Bson.Serialization.Attributes;
3using MongoDB.Bson.Serialization.Conventions;
4using MongoDB.Driver;
5
6public class vectorSearchBasicQuery
7{
8 // define connection to your Atlas cluster
9 private const string MongoConnectionString = "<connection-string>";
10
11 public static void Main(string[] args){
12 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
13 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
14
15 // connect to your Atlas cluster
16 var mongoClient = new MongoClient(MongoConnectionString);
17
18 // define namespace
19 var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
20 var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
21
22 // define vector embeddings to search
23 var vector = new[] 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24 var options = new VectorSearchOptions<EmbeddedMovie>() {
25 IndexName = "vector_index",
26 NumberOfCandidates = 150
27 };
28
29 // run query
30 var results = moviesCollection.Aggregate()
31 .VectorSearch(movie => movie.Embedding, vector, 10, options)
32 .Project(Builders<EmbeddedMovie>.Projection
33 .Include(movie => movie.Title)
34 .Include(movie => movie.Plot)
35 .Exclude(movie => movie.Id)
36 .MetaVectorSearchScore(movie => movie.Score))
37 .ToList();
38
39 // print results
40 foreach (var movie in results)
41 {
42 Console.WriteLine(movie.ToJson());
43 }
44 }
45}
46
47[BsonIgnoreExtraElements]
48public class EmbeddedMovie
49{
50 [BsonIgnoreIfDefault]
51 public ObjectId Id { get; set; }
52 public string? Title { get; set; }
53 public string? Plot { get; set; }
54 [BsonElement("plot_embedding_voyage_3_large")]
55 public double[]? Embedding { get; set; }
56 public double Score { get; set; }
57}
1{ "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" : 0.7710106372833252 }
2{ "plot" : "A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.", "title" : "Retroactive", "score" : 0.76004797220230103 }
3{ "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" : 0.75768613815307617 }
4{ "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" : 0.75765615701675415 }
5{ "plot" : "After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.", "title" : "Back to the Future Part II", "score" : 0.7521393895149231 }
6{ "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" : 0.75099325180053711 }
7{ "plot" : "Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...", "title" : "Timerider: The Adventure of Lyle Swann", "score" : 0.75026428699493408 }
8{ "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" : 0.75025033950805664 }
9{ "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" : 0.74949634075164795 }
10{ "plot" : "A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.", "title" : "The Final Countdown", "score" : 0.7469133734703064 }

A query a seguir filtra os documentos para filmes lançados entre 01 de janeiro de 1955 e 01 de janeiro de 1975 antes de executar o Atlas Search semântica em relação aos dados vetoriais de amostra. Utiliza o operador $and para executar uma operação lógica AND das datas especificadas. Em seguida, ele pesquisa o campo plot_embedding_voyage_3_large nos documentos filtrados para 150 vizinhos mais próximos usando as incorporações vetoriais para a string playground e retorna 10 documentos nos resultados. A consulta também especifica um estágio $project para fazer o seguinte:

  • Exclua o campo _id e inclua apenas os campos plot, title e year nos resultados.

  • Adicione um campo denominado score que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.

1using System.Reflection.Emit;
2using MongoDB.Bson;
3using MongoDB.Bson.Serialization.Attributes;
4using MongoDB.Bson.Serialization.Conventions;
5using MongoDB.Driver;
6using MongoDB.Driver.Search;
7
8public class vectorSearchFilterQuery
9{
10 // define connection to your Atlas cluster
11 private const string MongoConnectionString = "<connection-string>";
12
13 public static void Main(string[] args){
14 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
15 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
16
17 // connect to your Atlas cluster
18 var mongoClient = new MongoClient(MongoConnectionString);
19
20 // define namespace
21 var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
22 var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
23
24 // define vector embeddings to search
25 var vector = new[] 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0.037876874,0.033142265,0.041607171,-0.011477841,0.031133642,-0.045050524,-0.003981376,-0.025394723,-0.031707536,0.024533885,0.032568373,0.028694602,-0.021090532,-0.019942747,0.054806691,-0.004483532,-0.020086221,-0.01599724,0.018651491,0.020373167,0.000504397,-0.007424728,-0.063702017,-0.014203828,0.030846696,-0.006599758,-0.027259871,-0.024103465,-0.020660114,-0.004985687,0.005559579,-0.034146577,-0.011549577,-0.00173961,0.008321434,0.007030177,-0.015638558};
26 // define filter
27 var yearGtFilter = Builders<EmbeddedMovie>.Filter.Gt("year", 1955);
28 var yearLtFilter = Builders<EmbeddedMovie>.Filter.Lt("year", 1975);
29 // define options
30 var options = new VectorSearchOptions<EmbeddedMovie>() {
31 Filter = Builders<EmbeddedMovie>.Filter.And(yearGtFilter, yearLtFilter),
32 IndexName = "vector_index",
33 NumberOfCandidates = 150
34 };
35
36 // run query
37 var results = moviesCollection.Aggregate()
38 .VectorSearch(m => m.Embedding, vector, 10, options)
39 .Project(Builders<EmbeddedMovie>.Projection
40 .Include(m => m.Title)
41 .Include(movie => movie.Plot)
42 .Include(movie => movie.Year)
43 .MetaVectorSearchScore(m => m.Score))
44 .ToList();
45
46 // print results
47 foreach (var movie in results)
48 {
49 Console.WriteLine(movie.ToJson());
50 }
51 }
52}
53
54[BsonIgnoreExtraElements]
55public class EmbeddedMovie
56{
57 [BsonIgnoreIfDefault]
58 public string Title { get; set; }
59 public string Plot { get; set; }
60 public int Year { get; set; }
61 [BsonElement("plot_embedding_voyage_3_large")]
62 public double[] Embedding { get; set; }
63 public double Score { get; set; }
64}
1{ "_id" : { "$oid" : "573a1396f29313caabce34d3" }, "plot" : "A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.", "title" : "Chitty Chitty Bang Bang", "year" : 1968, "score" : 0.74897128343582153 }
2{ "_id" : { "$oid" : "573a1396f29313caabce5648" }, "plot" : "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title" : "The Little Prince", "year" : 1974, "score" : 0.73460787534713745 }
3{ "_id" : { "$oid" : "573a1395f29313caabce1b8d" }, "plot" : "In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.", "title" : "In Search of the Castaways", "year" : 1962, "score" : 0.72815901041030884 }
4{ "_id" : { "$oid" : "573a1396f29313caabce43e1" }, "plot" : "An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.", "title" : "Bedknobs and Broomsticks", "year" : 1971, "score" : 0.72034913301467896 }
5{ "_id" : { "$oid" : "573a1396f29313caabce54e6" }, "plot" : "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", "title" : "Pastoral Hide and Seek", "year" : 1974, "score" : 0.71956634521484375 }
6{ "_id" : { "$oid" : "573a1395f29313caabce143b" }, "plot" : "In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...", "title" : "Peter Pan", "year" : 1960, "score" : 0.71950322389602661 }
7{ "_id" : { "$oid" : "573a1396f29313caabce49bf" }, "plot" : "When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ...", "title" : "The Cowboys", "year" : 1972, "score" : 0.71795231103897095 }
8{ "_id" : { "$oid" : "573a1394f29313caabce0951" }, "plot" : "When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war.", "title" : "The 7th Voyage of Sinbad", "year" : 1958, "score" : 0.71480333805084229 }
9{ "_id" : { "$oid" : "573a1394f29313caabce000d" }, "plot" : "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title" : "The Red Balloon", "year" : 1956, "score" : 0.71470648050308228 }
10{ "_id" : { "$oid" : "573a1394f29313caabce074d" }, "plot" : "This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...", "title" : "Perri", "year" : 1957, "score" : 0.71208715438842773 }

O MongoDB Vector Search filtra os documentos com base no valor do campo year, que varia entre 1955 e 1975. Ela retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.

Dica

Exemplos adicionais de filtro

O tutorial Como realizar uma pesquisa semântica em dados em seu cluster Atlas demonstra outros pré-filtros em queries de pesquisa semântica em relação aos dados incorporados na coleção sample_mflix.embedded_movies.

A query abaixo usa o estágio $vectorSearch para pesquisar o campo plot_embedding_voyage_3_large usando incorporações vetoriais para a string viagem no tempo. Ela considera até 150 vizinhos mais próximos, e retorna 10 documentos nos resultados. A query também especifica um estágio $project para fazer o seguinte:

  • Excluir o campo _id e inclua somente os campos plot e title nos resultados.

  • Adicione um campo chamado score que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.

1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/v2/bson"
9 "go.mongodb.org/mongo-driver/v2/mongo"
10 "go.mongodb.org/mongo-driver/v2/mongo/options"
11)
12
13func main() {
14 ctx := context.Background()
15
16 // Replace the placeholder with your Atlas connection string
17 const uri = "<connection-string>"
18
19 // Connect to your Atlas cluster
20 clientOptions := options.Client().ApplyURI(uri)
21 client, err := mongo.Connect(clientOptions)
22 if err != nil {
23 log.Fatalf("failed to connect to the server: %v", err)
24 }
25 defer func() { _ = client.Disconnect(ctx) }()
26
27 // Set the namespace
28 coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30 queryVector := [2048]float64{
31 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024262359,0.054507218,-0.009721561,0.005816319,-0.00206687,-0.008766023,0.017365867,-0.000737426,0.018362951,-0.023597637,-0.019110762,0.021935832,0.041545134,-0.020357117,-0.017615138,0.044868745,-0.018030589,-0.032405205,-0.050186522,-0.014540797,0.005213914,-0.006688767,0.047527634,0.040714234,
32 }
33
34 vectorSearchStage := bson.D{
35 {"$vectorSearch", bson.D{
36 {"index", "vector_index"},
37 {"path", "plot_embedding_voyage_3_large"},
38 {"queryVector", queryVector},
39 {"numCandidates", 150},
40 {"limit", 10},
41 }}}
42
43 projectStage := bson.D{
44 {"$project", bson.D{
45 {"_id", 0},
46 {"plot", 1},
47 {"title", 1},
48 {"score", bson.D{{"$meta", "vectorSearchScore"}}},
49 }}}
50
51 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
52 if err != nil {
53 log.Fatalf("failed to retrieve data from the server: %v", err)
54 }
55 // Display the results
56 type ProjectedMovieResult struct {
57 Title string `bson:"title"`
58 Plot string `bson:"plot"`
59 Score float64 `bson:"score"`
60 }
61
62 var results []ProjectedMovieResult
63 if err = cursor.All(ctx, &results); err != nil {
64 log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
65 }
66 for _, result := range results {
67 fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score)
68 }
69}
1Title: About Time
2Plot: 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.
3Score: 0.7710106372833252
4
5Title: Retroactive
6Plot: A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.
7Score: 0.760047972202301
8
9Title: A.P.E.X.
10Plot: 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...
11Score: 0.7576861381530762
12
13Title: Timecop
14Plot: 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.
15Score: 0.7576561570167542
16
17Title: Back to the Future Part II
18Plot: After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.
19Score: 0.7521393895149231
20
21Title: Thrill Seekers
22Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.
23Score: 0.7509932518005371
24
25Title: Timerider: The Adventure of Lyle Swann
26Plot: Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...
27Score: 0.7502642869949341
28
29Title: The Time Machine
30Plot: 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.
31Score: 0.7502503395080566
32
33Title: The Time Traveler's Wife
34Plot: 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.
35Score: 0.749496340751648
36
37Title: The Final Countdown
38Plot: A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.
39Score: 0.7469133734703064

A query a seguir filtra os documentos para filmes lançados entre 01 de janeiro de 1955 e 01 de janeiro de 1975 antes de executar o Atlas Search semântica em relação aos dados vetoriais de amostra. Utiliza o operador $and para executar uma operação lógica AND das datas especificadas. Em seguida, ele pesquisa o campo plot_embedding_voyage_3_large nos documentos filtrados para 150 vizinhos mais próximos usando as incorporações vetoriais para a string playground e retorna 10 documentos nos resultados. A consulta também especifica um estágio $project para fazer o seguinte:

  • Exclua o campo _id e inclua apenas os campos plot, title e year nos resultados.

  • Adicione um campo denominado score que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.

1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/v2/bson"
9 "go.mongodb.org/mongo-driver/v2/mongo"
10 "go.mongodb.org/mongo-driver/v2/mongo/options"
11)
12
13func main() {
14 ctx := context.Background()
15
16 // Replace the placeholder with your Atlas connection string
17 const uri = "<connection-string>"
18
19 // Connect to your Atlas cluster
20 clientOptions := options.Client().ApplyURI(uri)
21 client, err := mongo.Connect(clientOptions)
22 if err != nil {
23 log.Fatalf("failed to connect to the server: %v", err)
24 }
25 defer func() { _ = client.Disconnect(ctx) }()
26
27 // Set the namespace
28 coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30 queryVector := [2048]float64{
31 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32 }
33
34 vectorSearchStage := bson.D{
35 {"$vectorSearch", bson.D{
36 {"index", "vector_index"},
37 {"path", "plot_embedding_voyage_3_large"},
38 {"filter", bson.D{{
39 "$and", bson.A{
40 bson.D{{"year", bson.D{{"$gt", 1955}}}},
41 bson.D{{"year", bson.D{{"$lt", 1975}}}},
42 },
43 }}},
44 {"queryVector", queryVector},
45 {"numCandidates", 150},
46 {"limit", 10},
47 }}}
48
49 projectStage := bson.D{
50 {"$project", bson.D{
51 {"_id", 0},
52 {"plot", 1},
53 {"title", 1},
54 {"year", 1},
55 {"score", bson.D{{"$meta", "vectorSearchScore"}}},
56 }}}
57
58 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
59 if err != nil {
60 log.Fatalf("failed to retrieve data from the server: %v", err)
61 }
62 // display the results
63 type ProjectedMovieResult struct {
64 Title string `bson:"title"`
65 Plot string `bson:"plot"`
66 Year int32 `bson:"year"`
67 Score float64 `bson:"score"`
68 }
69
70 var results []ProjectedMovieResult
71 if err = cursor.All(ctx, &results); err != nil {
72 log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
73 }
74 for _, result := range results {
75 fmt.Printf("Title: %v \nPlot: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Year, result.Score)
76 }
77}
1Title: Chitty Chitty Bang Bang
2Plot: A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.
3Year: 1968
4Score: 0.7489712834358215
5
6Title: The Little Prince
7Plot: A pilot, stranded in the desert, meets a little boy who is a prince on a planet.
8Year: 1974
9Score: 0.7346078753471375
10
11Title: In Search of the Castaways
12Plot: In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.
13Year: 1962
14Score: 0.7281590104103088
15
16Title: Bedknobs and Broomsticks
17Plot: An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.
18Year: 1971
19Score: 0.720349133014679
20
21Title: Pastoral Hide and Seek
22Plot: A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.
23Year: 1974
24Score: 0.7195663452148438
25
26Title: Peter Pan
27Plot: In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...
28Year: 1960
29Score: 0.7195032238960266
30
31Title: The Cowboys
32Plot: When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ...
33Year: 1972
34Score: 0.717952311038971
35
36Title: The 7th Voyage of Sinbad
37Plot: When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war.
38Year: 1958
39Score: 0.7148033380508423
40
41Title: The Red Balloon
42Plot: A red balloon with a life of its own follows a little boy around the streets of Paris.
43Year: 1956
44Score: 0.7147064805030823
45
46Title: Perri
47Plot: This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...
48Year: 1957
49Score: 0.7120871543884277

O MongoDB Vector Search filtra os documentos com base no valor do campo year, que varia entre 1955 e 1975. Ela retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.

Dica

Exemplos adicionais de filtro

O tutorial Como realizar uma pesquisa semântica em dados em seu cluster Atlas demonstra outros pré-filtros em queries de pesquisa semântica em relação aos dados incorporados na coleção sample_mflix.embedded_movies.

A query abaixo usa o estágio $vectorSearch para pesquisar o campo plot_embedding_voyage_3_large usando incorporações vetoriais para a string viagem no tempo. Ela considera até 150 vizinhos mais próximos, e retorna 10 documentos nos resultados. A query também especifica um estágio $project para fazer o seguinte:

  • Excluir o campo _id e inclua somente os campos plot e title nos resultados.

  • Adicione um campo chamado score que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.

1import com.mongodb.client.MongoClient;
2import com.mongodb.client.MongoClients;
3import com.mongodb.client.MongoCollection;
4import com.mongodb.client.MongoDatabase;
5import com.mongodb.client.model.search.FieldSearchPath;
6import org.bson.Document;
7import org.bson.conversions.Bson;
8
9import java.util.List;
10
11import static com.mongodb.client.model.Aggregates.project;
12import static com.mongodb.client.model.Aggregates.vectorSearch;
13import static com.mongodb.client.model.Projections.fields;
14import static com.mongodb.client.model.Projections.include;
15import static com.mongodb.client.model.Projections.exclude;
16import static com.mongodb.client.model.Projections.metaVectorSearchScore;
17import static com.mongodb.client.model.search.SearchPath.fieldPath;
18import static com.mongodb.client.model.search.VectorSearchOptions.approximateVectorSearchOptions;
19import static java.util.Arrays.asList;
20
21public class BasicQuery {
22 public static void main( String[] args ) {
23 // specify connection
24 String uri = "<connection-string>";
25
26 // establish connection and set namespace
27 try (MongoClient mongoClient = MongoClients.create(uri)) {
28 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
29 MongoCollection<Document> collection = database.getCollection("embedded_movies");
30
31
32 // define $vectorSearch query options
33 List<Double> queryVector = 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34 String indexName = "vector_index";
35 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding_voyage_3_large");
36 int limit = 10;
37 int numCandidates = 150;
38
39 // define pipeline
40 List<Bson> pipeline = asList(
41 vectorSearch(
42 fieldSearchPath,
43 queryVector,
44 indexName,
45 limit,
46 approximateVectorSearchOptions(numCandidates)),
47 project(
48 fields(exclude("_id"), include("title"), include("plot"),
49 metaVectorSearchScore("score"))));
50
51 // run query and print results
52 collection.aggregate(pipeline)
53 .forEach(doc -> System.out.println(doc.toJson()));
54 }
55 }
56}
1{"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": 0.7710106372833252}
2{"plot": "A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.", "title": "Retroactive", "score": 0.760047972202301}
3{"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": 0.7576861381530762}
4{"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": 0.7576561570167542}
5{"plot": "After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.", "title": "Back to the Future Part II", "score": 0.7521393895149231}
6{"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": 0.7509932518005371}
7{"plot": "Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...", "title": "Timerider: The Adventure of Lyle Swann", "score": 0.7502642869949341}
8{"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": 0.7502503395080566}
9{"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": 0.749496340751648}
10{"plot": "A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.", "title": "The Final Countdown", "score": 0.7469133734703064}

A query a seguir filtra os documentos para filmes lançados entre 01 de janeiro de 1955 e 01 de janeiro de 1975 antes de executar o Atlas Search semântica em relação aos dados vetoriais de amostra. Utiliza o operador $and para executar uma operação lógica AND das datas especificadas. Em seguida, ele pesquisa o campo plot_embedding_voyage_3_large nos documentos filtrados para 150 vizinhos mais próximos usando as incorporações vetoriais para a string playground e retorna 10 documentos nos resultados. A consulta também especifica um estágio $project para fazer o seguinte:

  • Exclua o campo _id e inclua apenas os campos plot, title e year nos resultados.

  • Adicione um campo denominado score que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.

1import static com.mongodb.client.model.Aggregates.*;
2import static com.mongodb.client.model.Projections.*;
3import static com.mongodb.client.model.search.SearchPath.fieldPath;
4import static java.util.Arrays.asList;
5import static com.mongodb.client.model.Filters.*;
6
7import com.mongodb.client.MongoClient;
8import com.mongodb.client.MongoClients;
9import com.mongodb.client.MongoCollection;
10import com.mongodb.client.MongoDatabase;
11import com.mongodb.client.model.search.FieldSearchPath;
12import com.mongodb.client.model.search.VectorSearchOptions;
13import org.bson.Document;
14import org.bson.conversions.Bson;
15
16import java.util.List;
17
18public class FilterQuery {
19 public static void main(String[] args) {
20 // Specify connection URI
21 String uri = "<connection-string>";
22
23 // Establish connection and set namespace
24 try (MongoClient mongoClient = MongoClients.create(uri)) {
25 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
26 MongoCollection<Document> collection = database.getCollection("embedded_movies");
27
28 // Parameters
29 List<Double> queryVector = 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30 String indexName = "vector_index";
31 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding_voyage_3_large");
32 long numCandidates = 150;
33 long limit = 10;
34
35 // Define filter criteria
36 Bson criteria = and(gt("year", 1955), lt("year", 1975));
37
38 // Create VectorSearchOptions
39 VectorSearchOptions options = VectorSearchOptions.approximateVectorSearchOptions(numCandidates)
40 .filter(criteria);
41
42 // Define the pipeline
43 List<Bson> pipeline = asList(
44 vectorSearch(
45 fieldSearchPath,
46 queryVector,
47 indexName,
48 limit,
49 options
50 ),
51 project(
52 fields(
53 exclude("_id"),
54 include("title"),
55 include("plot"),
56 include("year"),
57 metaVectorSearchScore("score")
58 )
59 )
60 );
61
62 // Run query and print results
63 collection.aggregate(pipeline).forEach(doc -> System.out.println(doc.toJson()));
64 }
65 }
66}
1{"plot": "A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.", "title": "Chitty Chitty Bang Bang", "year": 1968, "score": 0.7489712834358215}
2{"plot": "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title": "The Little Prince", "year": 1974, "score": 0.7346078753471375}
3{"plot": "In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.", "title": "In Search of the Castaways", "year": 1962, "score": 0.7281590104103088}
4{"plot": "An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.", "title": "Bedknobs and Broomsticks", "year": 1971, "score": 0.720349133014679}
5{"plot": "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", "title": "Pastoral Hide and Seek", "year": 1974, "score": 0.7195663452148438}
6{"plot": "In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...", "title": "Peter Pan", "year": 1960, "score": 0.7195032238960266}
7{"plot": "When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ...", "title": "The Cowboys", "year": 1972, "score": 0.717952311038971}
8{"plot": "When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war.", "title": "The 7th Voyage of Sinbad", "year": 1958, "score": 0.7148033380508423}
9{"plot": "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title": "The Red Balloon", "year": 1956, "score": 0.7147064805030823}
10{"plot": "This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...", "title": "Perri", "year": 1957, "score": 0.7120871543884277}

O MongoDB Vector Search filtra os documentos com base no valor do campo year, que varia entre 1955 e 1975. Ela retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.

Dica

Exemplos adicionais de filtro

O tutorial Como realizar uma pesquisa semântica em dados em seu cluster Atlas demonstra outros pré-filtros em queries de pesquisa semântica em relação aos dados incorporados na coleção sample_mflix.embedded_movies.

A query abaixo usa o estágio $vectorSearch para pesquisar o campo plot_embedding_voyage_3_large usando incorporações vetoriais para a string viagem no tempo. Ela considera até 150 vizinhos mais próximos, e retorna 10 documentos nos resultados. A query também especifica um estágio $project para fazer o seguinte:

  • Excluir o campo _id e inclua somente os campos plot e title nos resultados.

  • Adicione um campo chamado score que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async function run() {
9 try {
10 await client.connect();
11
12 // set namespace
13 const database = client.db("sample_mflix");
14 const coll = database.collection("embedded_movies");
15
16 // define pipeline
17 const agg = [
18 {
19 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding_voyage_3_large',
22 'queryVector': [
23 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24 ],
25 'numCandidates': 150,
26 'limit': 10
27 }
28 }, {
29 '$project': {
30 '_id': 0,
31 'plot': 1,
32 'title': 1,
33 'score': {
34 '$meta': 'vectorSearchScore'
35 }
36 }
37 }
38 ];
39 // run pipeline
40 const result = coll.aggregate(agg);
41
42 // print results
43 await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44 } finally {
45 await client.close();
46 }
47}
48run().catch(console.dir);
1'{"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":0.7710106372833252}'
2'{"plot":"A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.","title":"Retroactive","score":0.760047972202301}'
3'{"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":0.7576861381530762}'
4'{"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":0.7576561570167542}'
5'{"plot":"After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.","title":"Back to the Future Part II","score":0.7521393895149231}'
6'{"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":0.7509932518005371}'
7'{"plot":"Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...","title":"Timerider: The Adventure of Lyle Swann","score":0.7502642869949341}'
8'{"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":0.7502503395080566}'
9`{"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":0.749496340751648}`
10'{"plot":"A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.","title":"The Final Countdown","score":0.7469133734703064}'

A query a seguir filtra os documentos para filmes lançados entre 01 de janeiro de 1955 e 01 de janeiro de 1975 antes de executar o Atlas Search semântica em relação aos dados vetoriais de amostra. Utiliza o operador $and para executar uma operação lógica AND das datas especificadas. Em seguida, ele pesquisa o campo plot_embedding_voyage_3_large nos documentos filtrados para 150 vizinhos mais próximos usando as incorporações vetoriais para a string playground e retorna 10 documentos nos resultados. A consulta também especifica um estágio $project para fazer o seguinte:

  • Exclua o campo _id e inclua apenas os campos plot, title e year nos resultados.

  • Adicione um campo denominado score que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async function run() {
9 try {
10 await client.connect();
11
12 // set namespace
13 const database = client.db("sample_mflix");
14 const coll = database.collection("embedded_movies");
15
16 // define pipeline
17 const agg = [
18 {
19 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding_voyage_3_large',
22 'filter': {
23 '$and': [
24 {
25 'year': {
26 '$gt': 1955
27 }
28 },
29 {
30 'year': {
31 '$lt': 1975
32 }
33 }
34 ]
35 },
36 'queryVector': 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37 'numCandidates': 150,
38 'limit': 10
39 }
40 }, {
41 '$project': {
42 '_id': 0,
43 'title': 1,
44 'plot': 1,
45 'year': 1,
46 'score': {
47 '$meta': 'vectorSearchScore'
48 }
49 }
50 }
51 ];
52 // run pipeline
53 const result = coll.aggregate(agg);
54
55 // print results
56 await result.forEach((doc) => console.dir(JSON.stringify(doc)));
57 } finally {
58 await client.close();
59 }
60}
61run().catch(console.dir);
1'{"plot":"A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.","title":"Chitty Chitty Bang Bang","year":1968,"score":0.7492260932922363}'
2'{"plot":"A pilot, stranded in the desert, meets a little boy who is a prince on a planet.","title":"The Little Prince","year":1974,"score":0.734635591506958}'
3'{"plot":"In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.","title":"In Search of the Castaways","year":1962,"score":0.7282257080078125}'
4'{"plot":"An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.","title":"Bedknobs and Broomsticks","year":1971,"score":0.7203655242919922}'
5`{"plot":"A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.","title":"Pastoral Hide and Seek","year":1974,"score":0.7198004722595215}`
6'{"plot":"In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...","title":"Peter Pan","year":1960,"score":0.7196557521820068}'
7'{"plot":"When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ...","title":"The Cowboys","year":1972,"score":0.7186427116394043}'
8'{"plot":"When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war.","title":"The 7th Voyage of Sinbad","year":1958,"score":0.7148654460906982}'
9'{"plot":"A red balloon with a life of its own follows a little boy around the streets of Paris.","title":"The Red Balloon","year":1956,"score":0.7147994041442871}'
10'{"plot":"This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...","title":"Perri","year":1957,"score":0.712277889251709}'

O MongoDB Vector Search filtra os documentos com base no valor do campo year, que varia entre 1955 e 1975. Ela retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.

Dica

Exemplos adicionais de filtro

O tutorial Como realizar uma pesquisa semântica em dados em seu cluster Atlas demonstra outros pré-filtros em queries de pesquisa semântica em relação aos dados incorporados na coleção sample_mflix.embedded_movies.

A query abaixo usa o estágio $vectorSearch para pesquisar o campo plot_embedding_voyage_3_large usando incorporações vetoriais para a string viagem no tempo. Ela considera até 150 vizinhos mais próximos, e retorna 10 documentos nos resultados. A query também especifica um estágio $project para fazer o seguinte:

  • Excluir o campo _id e inclua somente os campos plot e title nos resultados.

  • Adicione um campo chamado score que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.

Trabalhe com uma versão executável deste exemplo como um bloco de anotações Python.

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8 {
9 '$vectorSearch': {
10 'index': 'vector_index',
11 'path': 'plot_embedding_voyage_3_large',
12 'queryVector': 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13 'numCandidates': 150,
14 'limit': 10
15 }
16 }, {
17 '$project': {
18 '_id': 0,
19 'plot': 1,
20 'title': 1,
21 'score': {
22 '$meta': 'vectorSearchScore'
23 }
24 }
25 }
26]
27
28# run pipeline
29result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
30
31# print results
32for i in result:
33 print(i)
34
1{'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': 0.7710106372833252}
2{'plot': 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', 'title': 'Retroactive', 'score': 0.760047972202301}
3{'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': 0.7576861381530762}
4{'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': 0.7576561570167542}
5{'plot': 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.', 'title': 'Back to the Future Part II', 'score': 0.7521393895149231}
6{'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': 0.7509932518005371}
7{'plot': 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...', 'title': 'Timerider: The Adventure of Lyle Swann', 'score': 0.7502642869949341}
8{'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': 0.7502503395080566}
9{'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': 0.749496340751648}
10{'plot': 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', 'title': 'The Final Countdown', 'score': 0.7469133734703064}

A query a seguir filtra os documentos para filmes lançados entre 01 de janeiro de 1955 e 01 de janeiro de 1975 antes de executar o Atlas Search semântica em relação aos dados vetoriais de amostra. Utiliza o operador $and para executar uma operação lógica AND das datas especificadas. Em seguida, ele pesquisa o campo plot_embedding_voyage_3_large nos documentos filtrados para 150 vizinhos mais próximos usando as incorporações vetoriais para a string playground e retorna 10 documentos nos resultados. A consulta também especifica um estágio $project para fazer o seguinte:

  • Exclua o campo _id e inclua apenas os campos plot, title e year nos resultados.

  • Adicione um campo denominado score que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.

Trabalhe com uma versão executável deste exemplo como um bloco de anotações Python.

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8 {
9 '$vectorSearch': {
10 'index': 'vector_index',
11 'path': 'plot_embedding_voyage_3_large',
12 'filter': {
13 '$and': [
14 {
15 'year': {
16 '$gt': 1955
17 }
18 },
19 {
20 'year': {
21 '$lt': 1975
22 }
23 }
24 ]
25 },
26 'queryVector': 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27 'numCandidates': 150,
28 'limit': 10
29 }
30 }, {
31 '$project': {
32 '_id': 0,
33 'title': 1,
34 'plot': 1,
35 'year': 1,
36 'score': {
37 '$meta': 'vectorSearchScore'
38 }
39 }
40 }
41]
42
43# run pipeline
44result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
45
46# print results
47for i in result:
48 print(i)
49
1{'plot': 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.', 'title': 'Chitty Chitty Bang Bang', 'year': 1968, 'score': 0.7492260932922363}
2{'plot': 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.', 'title': 'The Little Prince', 'year': 1974, 'score': 0.734635591506958}
3{'plot': 'In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.', 'title': 'In Search of the Castaways', 'year': 1962, 'score': 0.7282257080078125}
4{'plot': 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.', 'title': 'Bedknobs and Broomsticks', 'year': 1971, 'score': 0.7203655242919922}
5{'plot': "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", 'title': 'Pastoral Hide and Seek', 'year': 1974, 'score': 0.7198004722595215}
6{'plot': 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...', 'title': 'Peter Pan', 'year': 1960, 'score': 0.7196557521820068}
7{'plot': 'When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ...', 'title': 'The Cowboys', 'year': 1972, 'score': 0.7186427116394043}
8{'plot': 'When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war.', 'title': 'The 7th Voyage of Sinbad', 'year': 1958, 'score': 0.7148654460906982}
9{'plot': 'A red balloon with a life of its own follows a little boy around the streets of Paris.', 'title': 'The Red Balloon', 'year': 1956, 'score': 0.7147994041442871}
10{'plot': 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...', 'title': 'Perri', 'year': 1957, 'score': 0.712277889251709}

O MongoDB Vector Search filtra os documentos com base no valor do campo year, que varia entre 1955 e 1975. Ela retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.

Dica

Exemplos adicionais de filtro

O tutorial Como realizar uma pesquisa semântica em dados em seu cluster Atlas demonstra outros pré-filtros em queries de pesquisa semântica em relação aos dados incorporados na coleção sample_mflix.embedded_movies.

A query abaixo usa o estágio $vectorSearch para pesquisar o campo plot_embedding_voyage_3_large usando incorporações vetoriais para a string guerra mundial. Ele solicita correspondências exatas e limita os resultados apenas aos documentos 10. A query também especifica um estágio $project para fazer o seguinte:

  • Exclua o campo _id e inclua somente os campos plot, title e year nos resultados.

  • Adicione um campo denominado score que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.

  1. Salve as seguintes incorporações em um arquivo chamado query-embeddings.js:

    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  2. Carregue o arquivo em mongosh para usar as incorporações na sua query:

    load('/<path-to-file>/query-embeddings.js');
  3. Execute a seguinte query.

    1db.embedded_movies.aggregate([
    2 {
    3 "$vectorSearch": {
    4 "index": "vector_index",
    5 "path": "plot_embedding_voyage_3_large",
    6 "queryVector": WORLD_WAR_EMBEDDING,
    7 "exact": true,
    8 "limit": 10
    9 }
    10 },
    11 {
    12 "$project": {
    13 "_id": 0,
    14 "plot": 1,
    15 "title": 1,
    16 "score": { $meta: "vectorSearchScore" }
    17 }
    18 }
    19])
    1[
    2 {
    3 plot: 'During WW2 the Russian Army sent a special group named "Zvezda" to fight the Nazis in their backyard.',
    4 title: 'The Star',
    5 score: 0.7459506392478943
    6 },
    7 {
    8 plot: "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.",
    9 title: 'P-51 Dragon Fighter',
    10 score: 0.7430292367935181
    11 },
    12 {
    13 plot: 'April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.',
    14 title: 'Fury',
    15 score: 0.7428240776062012
    16 },
    17 {
    18 plot: "It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...",
    19 title: 'Memphis Belle',
    20 score: 0.7340154647827148
    21 },
    22 {
    23 plot: 'The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...',
    24 title: 'The Young Lions',
    25 score: 0.7327103614807129
    26 },
    27 {
    28 plot: 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.',
    29 title: 'White Tiger',
    30 score: 0.732368528842926
    31 },
    32 {
    33 plot: 'The story of ordinary men during WWII as seen from three different points of View.',
    34 title: 'The Fallen',
    35 score: 0.7301670908927917
    36 },
    37 {
    38 plot: 'A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.',
    39 title: 'War Pigs',
    40 score: 0.7290490865707397
    41 },
    42 {
    43 plot: 'Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...',
    44 title: 'The Red Baron',
    45 score: 0.728823184967041
    46 },
    47 {
    48 plot: 'In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.',
    49 title: 'Battle of Britain',
    50 score: 0.7270985245704651
    51 }
    52]
1using MongoDB.Bson;
2using MongoDB.Bson.Serialization.Attributes;
3using MongoDB.Bson.Serialization.Conventions;
4using MongoDB.Driver;
5using MongoDB.Driver.Search;
6
7public class vectorSearchBasicQuery
8{
9 // define connection to your Atlas cluster
10 private const string MongoConnectionString = "<connection-string>";
11
12 public static void Main(string[] args){
13 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
14 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
15
16 // connect to your Atlas cluster
17 var mongoClient = new MongoClient(MongoConnectionString);
18
19 // define namespace
20 var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
21 var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
22
23 // define vector embeddings to search
24 var vector = new[] 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7938282,-0.001239097,0.00737414,0.029335376,0.014828872,0.043519516,-0.029335376,-0.04287478,0.017166032,-0.015957156,0.004210916,0.029335376,-0.035299163,0.032559045,-0.02095384,0.026756443,-0.023210408,-0.001037618,0.008220353,-0.031914312,0.002216272,-0.030141294,0.007092069,0.015554197,-0.001229023,-0.014022955,-0.002981893,-0.006326448,-0.034976795,0.012411121,-0.024499875,-0.026111709,0.015070647,0.036427446,0.01523183,0.060282588};
25 var options = new VectorSearchOptions<EmbeddedMovie>() {
26 IndexName = "vector_index",
27 Exact = true
28 };
29
30 // run query
31 var results = moviesCollection.Aggregate()
32 .VectorSearch(m => m.Embedding, vector, 10, options)
33 .Project(Builders<EmbeddedMovie>.Projection
34 .Include(m => m.Title)
35 .Include(movie => movie.Plot))
36 .ToList();
37
38 // print results
39 foreach (var movie in results)
40 {
41 Console.WriteLine(movie.ToJson());
42 }
43 }
44}
45
46[BsonIgnoreExtraElements]
47public class EmbeddedMovie
48{
49 [BsonIgnoreIfDefault]
50 public string Title { get; set; }
51 public string Plot { get; set; }
52 [BsonElement("plot_embedding_voyage_3_large")]
53 public double[] Embedding { get; set; }
54}
1{ "_id" : { "$oid" : "573a13aaf29313caabd2144d" }, "plot" : "During WW2 the Russian Army sent a special group named \"Zvezda\" to fight the Nazis in their backyard.", "title" : "The Star" }
2{ "_id" : { "$oid" : "573a13e1f29313caabdbc094" }, "plot" : "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", "title" : "P-51 Dragon Fighter" }
3{ "_id" : { "$oid" : "573a13e1f29313caabdbbce8" }, "plot" : "April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.", "title" : "Fury" }
4{ "_id" : { "$oid" : "573a1399f29313caabcec130" }, "plot" : "It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...", "title" : "Memphis Belle" }
5{ "_id" : { "$oid" : "573a1394f29313caabce0d70" }, "plot" : "The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...", "title" : "The Young Lions" }
6{ "_id" : { "$oid" : "573a13daf29313caabdad76b" }, "plot" : "Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.", "title" : "White Tiger" }
7{ "_id" : { "$oid" : "573a13aef29313caabd2e8d0" }, "plot" : "The story of ordinary men during WWII as seen from three different points of View.", "title" : "The Fallen" }
8{ "_id" : { "$oid" : "573a13f1f29313caabddb924" }, "plot" : "A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.", "title" : "War Pigs" }
9{ "_id" : { "$oid" : "573a13adf29313caabd2a8cf" }, "plot" : "Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...", "title" : "The Red Baron" }
10{ "_id" : { "$oid" : "573a1396f29313caabce39a8" }, "plot" : "In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.", "title" : "Battle of Britain" }
1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/v2/bson"
9 "go.mongodb.org/mongo-driver/v2/mongo"
10 "go.mongodb.org/mongo-driver/v2/mongo/options"
11)
12
13func main() {
14 ctx := context.Background()
15
16 // Replace the placeholder with your Atlas connection string
17 const uri = "<connection-string>"
18
19 // Connect to your Atlas cluster
20 clientOptions := options.Client().ApplyURI(uri)
21 client, err := mongo.Connect(clientOptions)
22 if err != nil {
23 log.Fatalf("failed to connect to the server: %v", err)
24 }
25 defer func() { _ = client.Disconnect(ctx) }()
26
27 // Set the namespace
28 coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30 queryVector := [2048]float64{
31 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32 }
33
34 vectorSearchStage := bson.D{
35 {"$vectorSearch", bson.D{
36 {"index", "vector_index"},
37 {"path", "plot_embedding_voyage_3_large"},
38 {"queryVector", queryVector},
39 {"exact", true},
40 {"limit", 10},
41 }}}
42
43 projectStage := bson.D{
44 {"$project", bson.D{
45 {"_id", 0},
46 {"plot", 1},
47 {"title", 1},
48 {"score", bson.D{{"$meta", "vectorSearchScore"}}},
49 }}}
50
51 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
52 if err != nil {
53 log.Fatalf("failed to retrieve data from the server: %v", err)
54 }
55 // display the results
56 type ProjectedMovieResult struct {
57 Title string `bson:"title"`
58 Plot string `bson:"plot"`
59 Score float64 `bson:"score"`
60 }
61
62 var results []ProjectedMovieResult
63 if err = cursor.All(ctx, &results); err != nil {
64 log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
65 }
66 for _, result := range results {
67 fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score)
68 }
69}
1Title: The Star
2Plot: During WW2 the Russian Army sent a special group named "Zvezda" to fight the Nazis in their backyard.
3Score: 0.7459506392478943
4
5Title: P-51 Dragon Fighter
6Plot: As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.
7Score: 0.7430292367935181
8
9Title: Fury
10Plot: April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.
11Score: 0.7428240776062012
12
13Title: Memphis Belle
14Plot: It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...
15Score: 0.7340154647827148
16
17Title: The Young Lions
18Plot: The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...
19Score: 0.7327103614807129
20
21Title: White Tiger
22Plot: Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.
23Score: 0.732368528842926
24
25Title: The Fallen
26Plot: The story of ordinary men during WWII as seen from three different points of View.
27Score: 0.7301670908927917
28
29Title: War Pigs
30Plot: A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.
31Score: 0.7290490865707397
32
33Title: The Red Baron
34Plot: Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...
35Score: 0.728823184967041
36
37Title: Battle of Britain
38Plot: In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.
39Score: 0.7270985245704651
1import static com.mongodb.client.model.Aggregates.*;
2import static com.mongodb.client.model.Projections.*;
3import static com.mongodb.client.model.search.SearchPath.fieldPath;
4import static java.util.Arrays.asList;
5
6import com.mongodb.client.MongoClient;
7import com.mongodb.client.MongoClients;
8import com.mongodb.client.MongoCollection;
9import com.mongodb.client.MongoDatabase;
10import com.mongodb.client.model.search.FieldSearchPath;
11import com.mongodb.client.model.search.VectorSearchOptions;
12import org.bson.Document;
13import org.bson.conversions.Bson;
14
15import java.util.List;
16
17public class FilterQuery {
18 public static void main(String[] args) {
19 // Specify connection URI
20 String uri = "<connection-string>";
21
22 // Establish MongoDB connection
23 try (MongoClient mongoClient = MongoClients.create(uri)) {
24 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
25 MongoCollection<Document> collection = database.getCollection("embedded_movies");
26
27 // Parameters
28 List<Double> queryVector = 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29 String indexName = "vector_index";
30 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding_voyage_3_large");
31 long limit = 10;
32
33 // Create ExactVectorSearchOptions for precise matching
34 VectorSearchOptions options = VectorSearchOptions.exactVectorSearchOptions();
35
36 // Define the aggregation pipeline
37 List<Bson> pipeline = asList(
38 vectorSearch(
39 fieldSearchPath,
40 queryVector,
41 indexName,
42 limit,
43 options
44 ),
45 project(
46 fields(
47 exclude("_id"),
48 include("title"),
49 include("plot"),
50 metaVectorSearchScore("score")
51 )
52 )
53 );
54
55 // Run aggregation query and print results
56 collection.aggregate(pipeline).forEach(doc -> System.out.println(doc.toJson()));
57 }
58 }
59}
1{"plot": "During WW2 the Russian Army sent a special group named \"Zvezda\" to fight the Nazis in their backyard.", "title": "The Star", "score": 0.7459505200386047}
2{"plot": "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", "title": "P-51 Dragon Fighter", "score": 0.7430291771888733}
3{"plot": "April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.", "title": "Fury", "score": 0.7428240180015564}
4{"plot": "It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...", "title": "Memphis Belle", "score": 0.7340154647827148}
5{"plot": "The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...", "title": "The Young Lions", "score": 0.7327103614807129}
6{"plot": "Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.", "title": "White Tiger", "score": 0.7323684692382812}
7{"plot": "The story of ordinary men during WWII as seen from three different points of View.", "title": "The Fallen", "score": 0.7301670908927917}
8{"plot": "A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.", "title": "War Pigs", "score": 0.7290489673614502}
9{"plot": "Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...", "title": "The Red Baron", "score": 0.7288231253623962}
10{"plot": "In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.", "title": "Battle of Britain", "score": 0.7270984649658203}
1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async function run() {
9 try {
10 await client.connect();
11
12 // set namespace
13 const database = client.db("sample_mflix");
14 const coll = database.collection("embedded_movies");
15
16 // define pipeline
17 const agg = [
18 {
19 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding_voyage_3_large',
22 'queryVector': [
23 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24 ],
25 'exact': true,
26 'limit': 10
27 }
28 }, {
29 '$project': {
30 '_id': 0,
31 'plot': 1,
32 'title': 1,
33 'score': {
34 '$meta': 'vectorSearchScore'
35 }
36 }
37 }
38 ];
39 // run pipeline
40 const result = coll.aggregate(agg);
41
42 // print results
43 await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44 } finally {
45 await client.close();
46 }
47}
48run().catch(console.dir);
1'{"plot":"During WW2 the Russian Army sent a special group named \\"Zvezda\\" to fight the Nazis in their backyard.","title":"The Star","score":0.7459506392478943}'
2`{"plot":"As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.","title":"P-51 Dragon Fighter","score":0.7430292367935181}`
3'{"plot":"April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.","title":"Fury","score":0.7428240776062012}'
4`{"plot":"It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...","title":"Memphis Belle","score":0.7340154647827148}`
5'{"plot":"The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...","title":"The Young Lions","score":0.7327103614807129}'
6'{"plot":"Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.","title":"White Tiger","score":0.732368528842926}'
7'{"plot":"The story of ordinary men during WWII as seen from three different points of View.","title":"The Fallen","score":0.7301670908927917}'
8'{"plot":"A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.","title":"War Pigs","score":0.7290490865707397}'
9'{"plot":"Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...","title":"The Red Baron","score":0.728823184967041}'
10'{"plot":"In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.","title":"Battle of Britain","score":0.7270985245704651}'

Trabalhe com uma versão executável deste exemplo como um bloco de anotações Python.

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8 {
9 '$vectorSearch': {
10 'index': 'vector_index',
11 'path': 'plot_embedding_voyage_3_large',
12 'queryVector': 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13 'exact': True,
14 'limit': 10
15 }
16 }, {
17 '$project': {
18 '_id': 0,
19 'plot': 1,
20 'title': 1,
21 'score': {
22 '$meta': 'vectorSearchScore'
23 }
24 }
25 }
26]
27
28# run pipeline
29result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
30
31# print results
32for i in result:
33 print(i)
34
1{'plot': 'During WW2 the Russian Army sent a special group named "Zvezda" to fight the Nazis in their backyard.', 'title': 'The Star', 'score': 0.7459506392478943}
2{'plot': "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", 'title': 'P-51 Dragon Fighter', 'score': 0.7430292367935181}
3{'plot': 'April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.', 'title': 'Fury', 'score': 0.7428240776062012}
4{'plot': "It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...", 'title': 'Memphis Belle', 'score': 0.7340154647827148}
5{'plot': 'The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...', 'title': 'The Young Lions', 'score': 0.7327103614807129}
6{'plot': 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.', 'title': 'White Tiger', 'score': 0.732368528842926}
7{'plot': 'The story of ordinary men during WWII as seen from three different points of View.', 'title': 'The Fallen', 'score': 0.7301670908927917}
8{'plot': 'A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.', 'title': 'War Pigs', 'score': 0.7290490865707397}
9{'plot': 'Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...', 'title': 'The Red Baron', 'score': 0.728823184967041}
10{'plot': 'In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.', 'title': 'Battle of Britain', 'score': 0.7270985245704651}

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