Executar queries no Vector Search
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As queries do Atlas Vector Search têm a forma de um aggregation pipeline stage. Para as $vectorSearch queries, o Atlas Vector Search retorna os resultados da sua semântica Atlas Search.
Observação
Definição
O estágio $vectorSearch executa uma pesquisa de ANN ou ENN em um vetor no campo especificado.
Pesquisa ANN
Para a pesquisa ANN, o Atlas Vector Search encontra incorporações vetoriais em seus dados que estão mais próximas da incorporação vetorial em sua query com base em sua proximidade no espaço multidimensional e com base no número de vizinhos que ela considera. Ele usa o algoritmo Hierarchical Navigable Small Worlds e encontra as incorporações vetoriais mais semelhantes à incorporação vetorial em sua query sem escanear todos os vetores. Portanto, a pesquisa ANN é ideal para executar queries de grandes conjuntos de dados sem filtragem significativa.
Pesquisa ENN
Para a pesquisa ENN, o Atlas Vector Search pesquisa exaustivamente todas as incorporações vetoriais indexadas calculando a distância entre todas as incorporações e encontra o vizinho exato mais próximo da incorporação vetorial em sua query. Isso é computacionalmente intensivo e pode afetar negativamente a latência da query. Portanto, recomendamos pesquisas ENN para os seguintes casos de uso:
Você deseja determinar a recuperação 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.
Sintaxe
O campo que você deseja pesquisar deve ser indexado como tipo vetor do Atlas Vector Search dentro de um tipo de índice vectorSearch.
$vectorSearchUm pipeline
$vectorSearchtem a seguinte forma de protótipo:{ "$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>] } }
Campos
O estágio $vectorSearch recebe um documento com os seguintes campos:
Campo | Tipo | necessidade | Descrição |
|---|---|---|---|
| booleano | Opcional | Isso é necessário se Sinalizador que especifica se a pesquisa ENN ou ANN deve ser executada. O valor pode ser um dos seguintes:
Se omitido, o padrão é O Atlas Vector Search oferece suporte à pesquisa ANN em clusters que executam MongoDB v6.0.11, v7.0.2 ou posterior e pesquisa ENN em clusters executando MongoDB v6.0.16, v7.0.10, v7.3.2, ou posterior. |
| documento | Opcional | Qualquer expressão de correspondência MQL que compare um campo indexado com um booleano, data, objectId, número (não decimal), string ou UUID para usar como pré-filtro. Para saber quais operadores de query e pipeline de agregação o Atlas Vector Search suporta em seu filtro, consulte Pré-filtro do Atlas Vector Search . |
| string | Obrigatório | Nome do índice do Atlas Vector Search para usar. Atlas Vector Search não retorna resultados se você digitar incorretamente o nome do index ou se o index especificado ainda não existir no cluster. |
| número | Obrigatório | Número (somente do tipo |
| número | Opcional | Este campo é obrigatório se Número de vizinhos mais próximos a serem utilizados durante a pesquisa. O valor deve ser menor ou igual a ( Recomendamos que você especifique um número maior que o número de documentos a serem devolvidos ( |
| string | Obrigatório | Campo do tipo vetor indexado para pesquisa. Para saber mais, consulte Criação de caminhos. |
| array de números | Obrigatório | Array de números do tipo BSON Para saber mais sobre como gerar vetores BSON BinData O tamanho da array deve corresponder ao número do vetor Você deve incorporar sua query com o mesmo modelo que utilizou para incorporar os dados. |
Comportamento
$vectorSearch deve ser o primeiro estágio de qualquer pipeline onde
aparece.
Índice do Atlas Vector Search
Você deve indexar os campos a serem pesquisados usando o estágio $vectorSearch dentro de uma definição de índice do tipo vectorSearch. Você pode indexar os seguintes tipos de campos em uma definição de índice do tipo Atlas Vector Search VectorSearch:
Campos que contêm incorporações vetoriais como tipo de vetor.
Campos que contêm valores booleanos, de data, objectId, numéricos, string e UUID como tipo de filtro para habilitar a pesquisa vetorial em dados pré-filtrados.
Para saber mais sobre esses tipos de campo do Atlas Vector Search, consulte Como indexar campos para o Vector Search.
Pontuação do Atlas Vector Search
O Atlas 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 que ele retorna. Para similaridades cosine e dotProduct , o Atlas Vector Search normaliza a pontuação para garantir que ela não seja negativa.
Para cosine, o Atlas Vector Search usa o seguinte algoritmo para normalizar a pontuação:
score = (1 + cosine(v1,v2)) / 2
Para dotProduct, o Atlas Vector Search usa o seguinte algoritmo para normalizar a pontuação:
score = (1 + dotProduct(v1,v2)) / 2
Esses algoritmos mostram que o Atlas Vector Search 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 Atlas Vector Search adiciona 1 à pontuação de similaridade para normalizar a pontuação para um intervalo [0, 2] e, em seguida, divide por 2 para garantir um valor entre 0 e 1.
Para similaridade de euclidean, o Atlas Vector Search usa o seguinte algoritmo para normalizar a pontuação e garantir um valor entre 0 e 1:
score = 1 / (1 + euclidean(v1,v2))
O algoritmo anterior mostra que o Atlas Vector Search normaliza a pontuação calculando a distância euclidiana, que é a distância entre o vetor de documento (v1) e o vetor de query (v2), que tem o intervalo [0, ∞], O Atlas Vector Search transforma a distância em uma pontuação de similaridade adicionando 1 à distância e, em seguida, divide 1 pela pontuação de similaridade para garantir um valor entre 0 e 1.
A pontuação atribuída a um documento retornado faz parte dos metadados do documento. 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 do Atlas Vector Search, use vectorSearchScore como o valor na expressão $meta . Ou seja, após o estágio $vectorSearch, no estágio $project, o campo score recebe a expressão $meta. A expressão exige o valor vectorSearchScore para retornar a pontuação dos documentos para a pesquisa vetorial.
Exemplo
1 db.<collection>.aggregate([ 2 { 3 "$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 dos seus dados não afeta a pontuação que o Atlas Vector Search retorna usando $vectorSearchScore para $vectorSearch queries.
Pré-filtro do Atlas Vector Search
A opção de filter $vectorSearch corresponde somente a valores booleanos, de data, objectId, numéricos, de string e UUID BSON. Você deve indexar os campos nos quais deseja filtrar os dados como o 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 Atlas Vector Search oferece suporte à opção $vectorSearch filter para as seguintes expressões de correspondência MQL:
A opção $vectorSearch filter aceita somente os seguintes operadores de pipeline de agregação:
Observação
A opção $vectorSearch filter não oferece suporte a outros operadores de query, operadores de pipeline de agregação ou operadores do Atlas Search.
Considerações
O Atlas Vector Search é compatível com 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 ] } } ] }
Limitações
$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:
$lookupsub-pipeline$unionWithsub-pipeline$facetestágio do pipeline
Você pode passar os resultados de $vectorSearch para esse estágio.
Clientes aceitos
Você pode executar$vectorSearchconsultas usando a interface do usuário do Atlas , mongoshe qualquer driver MongoDB .
Você também pode utilizar a Pesquisa de Vetor do Atlas com implantações locais do Atlas que você cria com o Atlas CLI. Para saber mais, consulte Criar uma implantação de Atlas local.
Paralelização da Execução de Consultas Entre Segmentos
Recomendamos nós de pesquisa dedicados para isolar o processamento de query de pesquisa vetorial. Você poderá ver um desempenho de query aprimorado nos nós de pesquisa dedicados. Observe que os sistemas de alta CPU podem proporcionar mais melhorias no desempenho. Quando o Atlas Vector Search é executado em nós de pesquisa, o Atlas Vector Search paraleliza a execução de queries em segmentos de dados.
A paralelização do processamento de consultas melhora o tempo de resposta em muitos casos, como consultas em grandes conjuntos de dados. O uso do paralelismo entre consultas durante o processamento de consultas do Atlas Vector Search utiliza mais recursos, mas melhora a latência de cada consulta individual.
Observação
O Atlas Vector Search não garante que cada consulta seja executada simultaneamente. Por exemplo, quando muitas consultas simultâneas estão na fila, o Atlas Vector Search pode voltar à execução de um único thread.
Você pode ver resultados inconsistentes para as mesmas queries sucessivas. Para atenuar isso, aumente o valor de numCandidates nas queries $vectorSearch.
Exemplos
As consultas a seguir pesquisam a coleção de queries sample_mflix.embedded_movies usando o estágio $vectorSearch. As queries pesquisam no campo plot_embedding, que contém incorporações criadas utilizando o modelo de incorporações text-embedding-ada-002 do OpenAI. Se você adicionou a coleção de amostra ao seu cluster Atlas e criou os índices de amostra da coleção, poderá executar as seguintes queries na coleção.
Observação
Colar queryVector do código de amostra no seu terminal pode demorar um pouco dependendo do seu computador.
➤ Use o menu suspenso Selecione a linguagem para definir a linguagem dos exemplos nesta página.
Exemplos de ANN
A query abaixo usa o estágio $vectorSearch para pesquisar o campo plot_embedding 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 $project para fazer o seguinte:
Excluir o campo
_ide inclua somente os camposplotetitlenos resultados.Adicione um campo chamado
scoreque exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding", 6 "queryVector": 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7 "numCandidates": 150, 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: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', 4 title: 'Thrill Seekers', 5 score: 0.7892671227455139 6 }, 7 { 8 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.', 9 title: 'About Time', 10 score: 0.7843604683876038 11 }, 12 { 13 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.', 14 title: 'The Time Machine', 15 score: 0.7801066637039185 16 }, 17 { 18 plot: "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", 19 title: 'Crusade in Jeans', 20 score: 0.7789170742034912 21 }, 22 { 23 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.', 24 title: 'Timecop', 25 score: 0.7771612405776978 26 }, 27 { 28 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...', 29 title: 'A.P.E.X.', 30 score: 0.7730885744094849 31 }, 32 { 33 plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 34 title: 'Men in Black 3', 35 score: 0.7712380886077881 36 }, 37 { 38 plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 39 title: 'Tomorrowland', 40 score: 0.7669923901557922 41 }, 42 { 43 plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 44 title: 'Love Story 2050', 45 score: 0.7649372816085815 46 }, 47 { 48 plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 49 title: 'The Portal', 50 score: 0.7640786170959473 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 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 $project para fazer o seguinte:
Exclua o campo
_ide inclua apenas os camposplot,titleeyearnos resultados.Adicione um campo denominado
scoreque mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector-search-tutorial", 5 "path": "plot_embedding", 6 "filter": { 7 "$and": [ 8 { 9 "year": { "$gt": 1955 } 10 }, 11 { 12 "year": { "$lt": 1975 } 13 } 14 ] 15 }, 16 "queryVector": 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17 "numCandidates": 150, 18 "limit": 10 19 } 20 }, 21 { 22 "$project": { 23 "_id": 0, 24 "title": 1, 25 "plot": 1, 26 "year": 1, 27 "score": { $meta: "vectorSearchScore" } 28 } 29 } 30 ])
1 [ 2 { 3 plot: '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 ...', 4 title: 'Peter Pan', 5 year: 1960, 6 score: 0.748110830783844 7 }, 8 { 9 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.', 10 title: 'Chitty Chitty Bang Bang', 11 year: 1968, 12 score: 0.7442465424537659 13 }, 14 { 15 plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 16 title: 'That Man from Rio', 17 year: 1964, 18 score: 0.7416020035743713 19 }, 20 { 21 plot: 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.', 22 title: 'The Little Prince', 23 year: 1974, 24 score: 0.7378944158554077 25 }, 26 { 27 plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.', 28 title: 'The Red Balloon', 29 year: 1956, 30 score: 0.7342712879180908 31 }, 32 { 33 plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.', 34 title: 'Willy Wonka & the Chocolate Factory', 35 year: 1971, 36 score: 0.7342107892036438 37 }, 38 { 39 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.', 40 title: 'Bedknobs and Broomsticks', 41 year: 1971, 42 score: 0.7339356541633606 43 }, 44 { 45 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.", 46 title: 'Pastoral Hide and Seek', 47 year: 1974, 48 score: 0.733299970626831 49 }, 50 { 51 plot: 'A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.', 52 title: 'The Three Musketeers', 53 year: 1973, 54 score: 0.7331198453903198 55 }, 56 { 57 plot: 'A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.', 58 title: 'Frosty', 59 year: 1964, 60 score: 0.7318308353424072 61 } 62 ]
O Atlas Vector Search filtra os documentos com base no valor do campo year que varia entre 1955 e 1975. Ele retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.
Dica
Veja também: Exemplos adicionais de filtros
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 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
_ide inclua somente os camposplotetitlenos resultados.Adicione um campo chamado
scoreque exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
1 using MongoDB.Bson; 2 using MongoDB.Bson.Serialization.Attributes; 3 using MongoDB.Bson.Serialization.Conventions; 4 using MongoDB.Driver; 5 6 public 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 [] 48 public class EmbeddedMovie 49 { 50 [] 51 public ObjectId Id { get; set; } 52 public string? Title { get; set; } 53 public string? Plot { get; set; } 54 [] 55 public double[]? Embedding { get; set; } 56 public double Score { get; set; } 57 }
1 { "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" : 0.78926712274551392 } 2 { "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" : 0.78436046838760376 } 3 { "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" : 0.78010666370391846 } 4 { "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : 0.77891707420349121 } 5 { "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" : 0.77716124057769775 } 6 { "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" : 0.77308857440948486 } 7 { "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : 0.77123808860778809 } 8 { "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland", "score" : 0.76699239015579224 } 9 { "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : 0.76493728160858154 } 10 { "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal", "score" : 0.76407861709594727 }
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 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
_ide inclua apenas os camposplot,titleeyearnos resultados.Adicione um campo denominado
scoreque mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
1 using System.Reflection.Emit; 2 using MongoDB.Bson; 3 using MongoDB.Bson.Serialization.Attributes; 4 using MongoDB.Bson.Serialization.Conventions; 5 using MongoDB.Driver; 6 using MongoDB.Driver.Search; 7 8 public 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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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 [] 55 public class EmbeddedMovie 56 { 57 [] 58 public string Title { get; set; } 59 public string Plot { get; set; } 60 public int Year { get; set; } 61 [] 62 public double[] Embedding { get; set; } 63 public double Score { get; set; } 64 }
1 { "_id" : ObjectId("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" : 0.74811083078384399 } 2 { "_id" : ObjectId("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" : 0.74424654245376587 } 3 { "_id" : ObjectId("573a1395f29313caabce23cb"), "plot" : "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "title" : "That Man from Rio", "year" : 1964, "score" : 0.74160200357437134 } 4 { "_id" : ObjectId("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" : 0.73789441585540771 } 5 { "_id" : ObjectId("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" : 0.73427128791809082 } 6 { "_id" : ObjectId("573a1396f29313caabce482f"), "plot" : "A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.", "title" : "Willy Wonka & the Chocolate Factory", "year" : 1971, "score" : 0.7342107892036438 } 7 { "_id" : ObjectId("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" : 0.7339356541633606 } 8 { "_id" : ObjectId("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" : 0.73329997062683105 } 9 { "_id" : ObjectId("573a1396f29313caabce582d"), "plot" : "A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.", "title" : "The Three Musketeers", "year" : 1973, "score" : 0.73311984539031982 } 10 { "_id" : ObjectId("573a1395f29313caabce243e"), "plot" : "A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.", "title" : "Frosty", "year" : 1964, "score" : 0.73183083534240723 }
O Atlas Vector Search filtra os documentos com base no valor do campo year que varia entre 1955 e 1975. Ele retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.
Dica
Veja também: Exemplos adicionais de filtros
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 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 $project para fazer o seguinte:
Excluir o campo
_ide inclua somente os camposplotetitlenos resultados.Adicione um campo chamado
scoreque exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/bson" 9 "go.mongodb.org/mongo-driver/mongo" 10 "go.mongodb.org/mongo-driver/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(ctx, clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [1536]float64{ 31 -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 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"plot_embedding"}, 38 {"queryVector", queryVector}, 39 {"numCandidates", 150}, 40 {"limit", 10}, 41 }}} 42 43 projectStage := bson.D{ 44 {"$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 }
1 Title: Thrill Seekers 2 Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters. 3 Score: 0.7892671227455139 4 5 Title: About Time 6 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. 7 Score: 0.7843604683876038 8 9 Title: The Time Machine 10 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. 11 Score: 0.7801066637039185 12 13 Title: Crusade in Jeans 14 Plot: After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques... 15 Score: 0.7789170742034912 16 17 Title: Timecop 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 Score: 0.7771612405776978 20 21 Title: A.P.E.X. 22 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... 23 Score: 0.7730885744094849 24 25 Title: Men in Black 3 26 Plot: Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history. 27 Score: 0.7712380886077881 28 29 Title: Tomorrowland 30 Plot: Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory. 31 Score: 0.7669923901557922 32 33 Title: Love Story 2050 34 Plot: With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life. 35 Score: 0.7649372816085815 36 37 Title: The Portal 38 Plot: A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ... 39 Score: 0.7640786170959473
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 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 $project para fazer o seguinte:
Exclua o campo
_ide inclua apenas os camposplot,titleeyearnos resultados.Adicione um campo denominado
scoreque mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/bson" 9 "go.mongodb.org/mongo-driver/mongo" 10 "go.mongodb.org/mongo-driver/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(ctx, clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [1536]float64{ 31 0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 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"$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 }
1 Title: Peter Pan 2 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 ... 3 Year: 1960 4 Score: 0.748110830783844 5 6 Title: Chitty Chitty Bang Bang 7 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. 8 Year: 1968 9 Score: 0.7442465424537659 10 11 Title: That Man from Rio 12 Plot: A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues. 13 Year: 1964 14 Score: 0.7416020035743713 15 16 Title: The Little Prince 17 Plot: A pilot, stranded in the desert, meets a little boy who is a prince on a planet. 18 Year: 1974 19 Score: 0.7378944158554077 20 21 Title: The Red Balloon 22 Plot: A red balloon with a life of its own follows a little boy around the streets of Paris. 23 Year: 1956 24 Score: 0.7342712879180908 25 26 Title: Willy Wonka & the Chocolate Factory 27 Plot: A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all. 28 Year: 1971 29 Score: 0.7342107892036438 30 31 Title: Bedknobs and Broomsticks 32 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. 33 Year: 1971 34 Score: 0.7339356541633606 35 36 Title: Pastoral Hide and Seek 37 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. 38 Year: 1974 39 Score: 0.733299970626831 40 41 Title: The Three Musketeers 42 Plot: A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends. 43 Year: 1973 44 Score: 0.7331198453903198 45 46 Title: Frosty 47 Plot: A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together. 48 Year: 1964 49 Score: 0.7318308353424072
O Atlas Vector Search filtra os documentos com base no valor do campo year que varia entre 1955 e 1975. Ele retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.
Dica
Veja também: Exemplos adicionais de filtros
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 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
_ide inclua somente os camposplotetitlenos resultados.Adicione um campo chamado
scoreque exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
1 import com.mongodb.client.MongoClient; 2 import com.mongodb.client.MongoClients; 3 import com.mongodb.client.MongoCollection; 4 import com.mongodb.client.MongoDatabase; 5 import com.mongodb.client.model.search.FieldSearchPath; 6 import org.bson.Document; 7 import org.bson.conversions.Bson; 8 9 import java.util.List; 10 11 import static com.mongodb.client.model.Aggregates.project; 12 import static com.mongodb.client.model.Aggregates.vectorSearch; 13 import static com.mongodb.client.model.Projections.fields; 14 import static com.mongodb.client.model.Projections.include; 15 import static com.mongodb.client.model.Projections.exclude; 16 import static com.mongodb.client.model.Projections.metaVectorSearchScore; 17 import static com.mongodb.client.model.search.SearchPath.fieldPath; 18 import static com.mongodb.client.model.search.VectorSearchOptions.approximateVectorSearchOptions; 19 import static java.util.Arrays.asList; 20 21 public 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 = (asList(-0.0016261312d, -0.028070757d, -0.011342932d, -0.012775794d, -0.0027440966d, 0.008683807d, -0.02575152d, -0.02020668d, -0.010283281d, -0.0041719596d, 0.021392956d, 0.028657231d, -0.006634482d, 0.007490867d, 0.018593878d, 0.0038187427d, 0.029590257d, -0.01451522d, 0.016061379d, 0.00008528442d, -0.008943722d, 0.01627464d, 0.024311995d, -0.025911469d, 0.00022596726d, -0.008863748d, 0.008823762d, -0.034921836d, 0.007910728d, -0.01515501d, 0.035801545d, -0.0035688248d, -0.020299982d, -0.03145631d, -0.032256044d, -0.028763862d, -0.0071576433d, -0.012769129d, 0.012322609d, 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-0.011982721d, -0.016967745d, -0.0060913274d, -0.007130985d, -0.013109017d, -0.009710136d)); 34 String indexName = "vector_index"; 35 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding"); 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": "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": 0.7892671227455139} 2 {"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": 0.7843604683876038} 3 {"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": 0.7801066637039185} 4 {"plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title": "Crusade in Jeans", "score": 0.7789170742034912} 5 {"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": 0.7771612405776978} 6 {"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": 0.7730885744094849} 7 {"plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title": "Men in Black 3", "score": 0.7712380886077881} 8 {"plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title": "Tomorrowland", "score": 0.7669923901557922} 9 {"plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title": "Love Story 2050", "score": 0.7649372816085815} 10 {"plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title": "The Portal", "score": 0.7640786170959473}
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 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
_ide inclua apenas os camposplot,titleeyearnos resultados.Adicione um campo denominado
scoreque mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
1 import static com.mongodb.client.model.Aggregates.*; 2 import static com.mongodb.client.model.Projections.*; 3 import static com.mongodb.client.model.search.SearchPath.fieldPath; 4 import static com.mongodb.client.model.search.VectorSearchOptions.vectorSearchOptions; 5 import static java.util.Arrays.asList; 6 7 import com.mongodb.client.MongoClient; 8 import com.mongodb.client.MongoClients; 9 import com.mongodb.client.MongoCollection; 10 import com.mongodb.client.MongoDatabase; 11 import com.mongodb.client.model.*; 12 import com.mongodb.client.model.search.VectorSearchOptions; 13 import java.util.List; 14 import com.mongodb.client.model.search.FieldSearchPath; 15 import org.bson.Document; 16 import org.bson.conversions.Bson; 17 18 public class FilterQuery { 19 public static void main( String[] args ) { 20 // specify connection 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 // define $vectorSearch query options 29 List<Double> queryVector = 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30 String indexName = "vector_index"; 31 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding"); 32 int numCandidates = 150; 33 int limit = 10; 34 Bson criteria = Filters.and(Filters.gt("year", 1955), Filters.lt("year", 1975)); 35 VectorSearchOptions options = vectorSearchOptions().filter(criteria); 36 37 // define pipeline 38 List<Bson> pipeline = asList( 39 vectorSearch( 40 fieldSearchPath, 41 queryVector, 42 indexName, 43 numCandidates, 44 limit, 45 options), 46 project( 47 fields(exclude("_id"), include("title"), include("plot"), include("year"), 48 metaVectorSearchScore("score")))); 49 50 // run query and print results 51 collection.aggregate(pipeline) 52 .forEach(doc -> System.out.println(doc.toJson())); 53 } 54 } 55 }
1 {"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": 0.748110830783844} 2 {"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": 0.7442465424537659} 3 {"plot": "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "title": "That Man from Rio", "year": 1964, "score": 0.7416020035743713} 4 {"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": 0.7378944158554077} 5 {"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": 0.7342712879180908} 6 {"plot": "A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.", "title": "Willy Wonka & the Chocolate Factory", "year": 1971, "score": 0.7342107892036438} 7 {"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": 0.7339356541633606} 8 {"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": 0.733299970626831} 9 {"plot": "A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.", "title": "The Three Musketeers", "year": 1973, "score": 0.7331198453903198} 10 {"plot": "A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.", "title": "Frosty", "year": 1964, "score": 0.7318308353424072}
O Atlas Vector Search filtra os documentos com base no valor do campo year que varia entre 1955 e 1975. Ele retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.
Dica
Veja também: Exemplos adicionais de filtros
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 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 $project para fazer o seguinte:
Excluir o campo
_ide inclua somente os camposplotetitlenos resultados.Adicione um campo chamado
scoreque exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding', 22 'queryVector': [ 23 -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, 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47 } 48 run().catch(console.dir);
1 '{"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":0.7892671227455139}' 2 '{"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":0.7843604683876038}' 3 '{"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":0.7801066637039185}' 4 `{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}` 5 '{"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":0.7771612405776978}' 6 '{"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":0.7730885744094849}' 7 `{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}` 8 '{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}' 9 '{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}' 10 '{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'
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 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 $project para fazer o seguinte:
Exclua o campo
_ide inclua apenas os camposplot,titleeyearnos resultados.Adicione um campo denominado
scoreque mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding', 22 'filter': { 23 '$and': [ 24 { 25 'year': { 26 '$gt': 1955 27 }, 28 'year': { 29 '$lt': 1975 30 } 31 } 32 ] 33 }, 34 'queryVector': [0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 0.010624337, -0.024236975, -0.0048659067, 0.015153068, -0.000490888, -0.022161031, -0.0024560927, -0.007411252, 0.009745035, -0.01886861, 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0.006518728, -0.0047766543, 0.0055501745, 0.039747052, -0.034061346, 0.049425974, 0.0023883271, -0.0035601775, 0.00863434, -0.003897353, 0.016237319, 0.006436087, -0.00037828952, -0.017797582, -0.019450404, 0.0009809496, 0.0036461244, 0.013176293, 0.0036461244, -0.01094829, -0.018260373, 0.00035246418, 0.012885396, -0.006796402, -0.015972868, 0.027899627, -0.0077021485, 0.027608732, 0.01696456, -0.0014486981, -0.017969476, 0.015642302, -0.00477996, -0.0048890463, -0.020058641, 0.008323609, 0.013017623, -0.01886861, -0.008204606, 0.016303431, -0.010029321, -0.001018138, -0.0332151, 0.010525168, 0.032871313, 0.011549917, 0.010928456, -0.014253933, -0.011384634, 0.00894507, 0.034616694, -0.016872002, -0.010987958, -0.011953205], 35 'numCandidates': 150, 36 'limit': 10 37 } 38 }, { 39 '$project': { 40 '_id': 0, 41 'title': 1, 42 'plot': 1, 43 'year': 1, 44 'score': { 45 '$meta': 'vectorSearchScore' 46 } 47 } 48 } 49 ]; 50 // run pipeline 51 const result = coll.aggregate(agg); 52 53 // print results 54 await result.forEach((doc) => console.dir(JSON.stringify(doc))); 55 } finally { 56 await client.close(); 57 } 58 } 59 run().catch(console.dir);
1 '{"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":0.748110830783844}' 2 '{"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":0.7442465424537659}' 3 '{"plot":"A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.","title":"That Man from Rio","year":1964,"score":0.7416020035743713}' 4 '{"plot":"A boy raised by wolves tries to adapt to human village life.","title":"Jungle Book","year":1942,"score":0.7387760877609253}' 5 '{"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":0.7378944158554077}' 6 '{"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":0.7342712879180908}' 7 '{"plot":"A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.","title":"Willy Wonka & the Chocolate Factory","year":1971,"score":0.7342107892036438}' 8 '{"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":0.7339356541633606}' 9 '{"plot":"Arriving home to find his native land under the yoke of corrupt merchants, an adventurer named Sadko sets sail in search of a mythical bird of happiness.","title":"Sadko","year":1953,"score":0.7339220643043518}' 10 '{"plot":"A young boy’s 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":0.733299970626831}';
O Atlas Vector Search filtra os documentos com base no valor do campo year que varia entre 1955 e 1975. Ele retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.
Dica
Veja também: Exemplos adicionais de filtros
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 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 $project para fazer o seguinte:
Excluir o campo
_ide inclua somente os camposplotetitlenos resultados.Adicione um campo chamado
scoreque exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
1 import pymongo 2 import datetime 3 4 # connect to your Atlas cluster 5 client = pymongo.MongoClient("<connection-string>") 6 7 # define pipeline 8 pipeline = [ 9 { 10 '$vectorSearch': { 11 'index': 'vector_index', 12 'path': 'plot_embedding', 13 'queryVector': [-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, 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0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136], 14 'numCandidates': 150, 15 'limit': 10 16 } 17 }, { 18 '$project': { 19 '_id': 0, 20 'plot': 1, 21 'title': 1, 22 'score': { 23 '$meta': 'vectorSearchScore' 24 } 25 } 26 } 27 ] 28 29 # run pipeline 30 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 31 32 # print results 33 for i in result: 34 print(i) 35
1 {'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': 0.7892671227455139} 2 {'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': 0.7843604683876038} 3 {'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': 0.7801066637039185} 4 {'plot': "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", 'title': 'Crusade in Jeans', 'score': 0.7789170742034912} 5 {'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': 0.7771612405776978} 6 {'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': 0.7730885744094849} 7 {'plot': "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 'title': 'Men in Black 3', 'score': 0.7712380886077881} 8 {'plot': 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 'title': 'Tomorrowland', 'score': 0.7669923901557922} 9 {'plot': 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 'title': 'Love Story 2050', 'score': 0.7649372816085815} 10 {'plot': 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 'title': 'The Portal', 'score': 0.7640786170959473}
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 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 $project para fazer o seguinte:
Exclua o campo
_ide inclua apenas os camposplot,titleeyearnos resultados.Adicione um campo denominado
scoreque mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
1 import pymongo 2 import datetime 3 4 # connect to your Atlas cluster 5 client = pymongo.MongoClient("<connection-string>") 6 7 # define pipeline 8 pipeline = [ 9 { 10 '$vectorSearch': { 11 'index': 'vector_index', 12 'path': 'plot_embedding', 13 'filter': { 14 '$and': [ 15 { 16 'year': { 17 '$gt': 1955 18 }, 19 'year': { 20 '$lt': 1975 21 } 22 } 23 ] 24 }, 25 'queryVector': [0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 0.010624337, -0.024236975, -0.0048659067, 0.015153068, -0.000490888, -0.022161031, -0.0024560927, -0.007411252, 0.009745035, -0.01886861, 0.02112967, -0.011939983, 0.015153068, 0.0025800543, 0.017824028, -0.02410475, -0.016633997, -0.0018214093, -0.008323609, -0.009222744, 0.009388026, -0.0028296304, 0.0017536436, 0.0065517845, -0.011635863, -0.028454976, -0.018934723, 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-0.01094829, -0.018260373, 0.00035246418, 0.012885396, -0.006796402, -0.015972868, 0.027899627, -0.0077021485, 0.027608732, 0.01696456, -0.0014486981, -0.017969476, 0.015642302, -0.00477996, -0.0048890463, -0.020058641, 0.008323609, 0.013017623, -0.01886861, -0.008204606, 0.016303431, -0.010029321, -0.001018138, -0.0332151, 0.010525168, 0.032871313, 0.011549917, 0.010928456, -0.014253933, -0.011384634, 0.00894507, 0.034616694, -0.016872002, -0.010987958, -0.011953205], 26 'numCandidates': 150, 27 'limit': 10 28 } 29 }, { 30 '$project': { 31 '_id': 0, 32 'title': 1, 33 'plot': 1, 34 'year': 1, 35 'score': { 36 '$meta': 'vectorSearchScore' 37 } 38 } 39 } 40 ] 41 42 # run pipeline 43 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 44 45 # print results 46 for i in result: 47 print(i) 48
1 {'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': 0.748110830783844} 2 {'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': 0.7442465424537659} 3 {'plot': 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 'title': 'That Man from Rio', 'year': 1964, 'score': 0.7416020035743713} 4 {'plot': 'A boy raised by wolves tries to adapt to human village life.', 'title': 'Jungle Book', 'year': 1942, 'score': 0.7387760877609253} 5 {'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': 0.7378944158554077} 6 {'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': 0.7342712879180908} 7 {'plot': 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.', 'title': 'Willy Wonka & the Chocolate Factory', 'year': 1971, 'score': 0.7342107892036438} 8 {'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': 0.7339356541633606} 9 {'plot': 'Arriving home to find his native land under the yoke of corrupt merchants, an adventurer named Sadko sets sail in search of a mythical bird of happiness.', 'title': 'Sadko', 'year': 1953, 'score': 0.7339220643043518} 10 {'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': 0.733299970626831}
O Atlas Vector Search filtra os documentos com base no valor do campo year que varia entre 1955 e 1975. Ele retorna documentos que resumem as aventuras infantis na trama de filmes lançados entre 1955 e 1975.
Dica
Veja também: Exemplos adicionais de filtros
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.
Exemplos de ENN
A query abaixo usa o estágio $vectorSearch para pesquisar o campo plot_embedding 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 $project para fazer o seguinte:
Exclua o campo
_ide inclua somente os camposplot,titleeyearnos resultados.Adicione um campo denominado
scoreque mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding", 6 "queryVector": 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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: 'It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.', 4 title: 'Red Dawn', 5 score: 0.7700583338737488 6 }, 7 { 8 plot: 'A dramatization of the World War II Battle of Iwo Jima.', 9 title: 'Sands of Iwo Jima', 10 score: 0.7581185102462769 11 }, 12 { 13 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.', 14 title: 'White Tiger', 15 score: 0.750884473323822 16 }, 17 { 18 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.", 19 title: 'P-51 Dragon Fighter', 20 score: 0.749922513961792 21 }, 22 { 23 plot: 'A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.', 24 title: 'When Trumpets Fade', 25 score: 0.7498313188552856 26 }, 27 { 28 plot: 'Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.', 29 title: 'Battletruck', 30 score: 0.7497193217277527 31 }, 32 { 33 plot: 'It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....', 34 title: 'Robot Jox', 35 score: 0.7495121955871582 36 }, 37 { 38 plot: 'During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.', 39 title: 'The Enemy Below', 40 score: 0.746050238609314 41 }, 42 { 43 plot: 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 44 title: 'Saints and Soldiers', 45 score: 0.7435222864151001 46 }, 47 { 48 plot: 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 49 title: 'Saints and Soldiers', 50 score: 0.743497371673584 51 } 52 ]
Not yet available.
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/bson" 9 "go.mongodb.org/mongo-driver/mongo" 10 "go.mongodb.org/mongo-driver/mongo/options" 11 ) 12 13 func 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(ctx, 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 := [1536]float64{ 31 -0.006954097, -0.009932499, -0.001311474, -0.021280076, -0.014608995, -0.008227668, -0.013274779, -0.0069271433, -0.009521453, -0.030943038, 0.017304381, 0.015094165, -0.010397454, 0.010943269, 0.012230316, 0.025943095, 0.02583528, 0.001368751, 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-0.00016856696, 0.006010712, 0.010781546, -0.0020670246, -0.021873062, -0.011859701, -0.00040346567, 0.008665668, 0.016199274, 0.04204803, -0.036091227, 0.032102056, 0.02043103, 0.008402867, 0.013766687, -0.020201921, 0.0054682656, -0.0037027872, -0.01221684, 0.0030036713, -0.027573805, 0.0054076193, 0.001863186, -0.006357743, -0.029137129, -0.0096697, 0.007675113, -0.0153637035, -0.014878534, 0.0060612503, 0.001300524, 0.030484822, 0.003800495, 0.018908137, 0.01840949, -0.00082461984, -0.030808268, 0.014918964, -0.013254563, -0.0036050796, -0.009319299, -0.011920347, -0.0059062657, -0.020714046, -0.0051347115, 0.02737165, 0.005198727, -0.0059601734, -0.027519897, 0.026967343, 0.0007357563, 0.010431146, 0.017654782, -0.019797614, -0.039137013, 0.033099346, 0.0023129785, -0.011017392, -0.0032816331, -0.020848814, 32 } 33 34 vectorSearchStage := bson.D{ 35 {"$vectorSearch", bson.D{ 36 {"index", "vector_index"}, 37 {"path", "plot_embedding"}, 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 }
1 Title: Red Dawn 2 Plot: It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces. 3 Score: 0.7700583338737488 4 5 Title: Sands of Iwo Jima 6 Plot: A dramatization of the World War II Battle of Iwo Jima. 7 Score: 0.7581185102462769 8 9 Title: White Tiger 10 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. 11 Score: 0.750884473323822 12 13 Title: P-51 Dragon Fighter 14 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. 15 Score: 0.749922513961792 16 17 Title: When Trumpets Fade 18 Plot: A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct. 19 Score: 0.7498313188552856 20 21 Title: Battletruck 22 Plot: Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age. 23 Score: 0.7497193217277527 24 25 Title: Robot Jox 26 Plot: It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories.... 27 Score: 0.7495121955871582 28 29 Title: The Enemy Below 30 Plot: During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time. 31 Score: 0.746050238609314 32 33 Title: Saints and Soldiers 34 Plot: Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre. 35 Score: 0.7435222864151001 36 37 Title: Saints and Soldiers 38 Plot: Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre. 39 Score: 0.743497371673584
Not yet available.
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async 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 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding', 22 'queryVector': [ 23 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24 ], 25 'exact': true, 26 'limit': 10 27 } 28 }, { 29 '$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 } 48 run().catch(console.dir);
1 { 2 "plot":"It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.", 3 "title":"Red Dawn", 4 "score":0.7700583338737488 5 } 6 { 7 "plot":"A dramatization of the World War II Battle of Iwo Jima.", 8 "title":"Sands of Iwo Jima", 9 "score":0.7581185102462769 10 } 11 { 12 "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.", 13 "title":"White Tiger", 14 "score":0.750884473323822 15 } 16 { 17 "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.", 18 "title":"P-51 Dragon Fighter", 19 "score":0.749922513961792 20 } 21 { 22 "plot":"A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.", 23 "title":"When Trumpets Fade", 24 "score":0.7498313188552856 25 } 26 { 27 "plot":"Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.", 28 "title":"Battletruck", 29 "score":0.7497193217277527 30 } 31 { 32 "plot":"It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....", 33 "title":"Robot Jox", 34 "score":0.7495121955871582 35 } 36 { 37 "plot":"During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.", 38 "title":"The Enemy Below", 39 "score":0.746050238609314 40 } 41 { 42 "plot":"Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.", 43 "title":"Saints and Soldiers", 44 "score":0.7435222864151001 45 } 46 { 47 "plot":"Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.", 48 "title":"Saints and Soldiers", 49 "score":0.743497371673584 50 }
1 import pymongo 2 import datetime 3 4 # connect to your Atlas cluster 5 client = pymongo.MongoClient("<connection-string>") 6 7 # define pipeline 8 pipeline = [ 9 { 10 '$vectorSearch': { 11 'index': 'vector_index', 12 'path': 'plot_embedding', 13 'queryVector': 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14 'exact': True, 15 'limit': 10 16 } 17 }, { 18 '$project': { 19 '_id': 0, 20 'plot': 1, 21 'title': 1, 22 'score': { 23 '$meta': 'vectorSearchScore' 24 } 25 } 26 } 27 ] 28 29 # run pipeline 30 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 31 32 # print results 33 for i in result: 34 print(i) 35
1 { 2 'plot': 'It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.', 3 'title': 'Red Dawn', 4 'score': 0.7700583338737488 5 } 6 { 7 'plot': 'A dramatization of the World War II Battle of Iwo Jima.', 8 'title': 'Sands of Iwo Jima', 9 'score': 0.7581185102462769 10 } 11 { 12 '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.', 13 'title': 'White Tiger', 14 'score': 0.750884473323822 15 } 16 { 17 '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.", 18 'title': 'P-51 Dragon Fighter', 19 'score': 0.749922513961792 20 } 21 { 22 'plot': 'A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.', 23 'title': 'When Trumpets Fade', 24 'score': 0.7498313188552856 25 } 26 { 27 'plot': 'Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.', 28 'score': 0.7497193217277527 29 } 30 { 31 'plot': 'It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....', 32 'title': 'Robot Jox', 33 'score': 0.7495121955871582 34 } 35 { 36 'plot': 'During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.', 37 'title': 'The Enemy Below', 38 'score': 0.746050238609314} 39 {'plot': 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 'title': 'Saints and Soldiers', 'score': 0.7435222864151001 40 } 41 { 42 'plot': 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 43 'title': 'Saints and Soldiers', 44 'score': 0.743497371673584 45 }