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.
Definição
O estágio $vectorSearch
executa uma pesquisa de ANN ou ENN em um vetor no campo especificado.
Pesquisa ANN
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.
Pesquisa ENN
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.
Sintaxe
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>] } }
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 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. |
| 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. |
| 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. |
| 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 especificar um número pelo menos 20 vezes maior que o número de documentos a serem retornados ( 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 |
| string | Obrigatório | Campo do tipo vetor indexado para pesquisa. |
| array de números | Obrigatório | Array de números de Para aprender mais sobre como gerar vetores BSON 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. 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 |
Clientes aceitos
Você pode executar$vectorSearch
consultas usando a interface do usuário do Atlas , mongosh
e 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.
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:
Você pode passar os resultados de $vectorSearch
para esse estágio.
Comportamento
$vectorSearch
deve ser o primeiro estágio de qualquer pipeline onde
aparece.
Índice de Vector Search do MongoDB
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.
Pontuação de Vector Search do MongoDB
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 $meta. A expressão exige o valor vectorSearchScore
para retornar a pontuação dos documentos para a pesquisa vetorial.
Exemplo
1 db.<collection>.aggregate([ 2 { 3 "$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 de seus dados não afeta a pontuação que o MongoDB Vector Search retorna usando $vectorSearchScore
para $vectorSearch
consultas.
Pré-filtro de Vector Search do MongoDB
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 |
Observação
A opção $vectorSearch
filter
não suporta outros operadores de query, operadores de pipeline de agregação ou operadores de pesquisa MongoDB.
Considerações
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 ] } } ] }
numCandidates
Seleção
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 delimit
exigem valores proporcionalmente mais altos denumCandidates
para manter o recall.Quantização de vetores: vetores quantizados reduzem o armazenamento ao custo da precisão. O uso de vetores quantizados (subtipos
int8
ouint1
) pode exigir valores denumCandidates
mais altos em comparação com vetores de precisão totalfloat32
para manter um recall semelhante.
Paralelização da Execução de Consultas Entre Segmentos
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
.
Exemplos
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.
Pré-requisitos
Antes de executar estes exemplos, realize as seguintes ações:
Adicione a coleção de amostra ao seu cluster Atlas.
Crie os índices de amostra para a coleção. Para instruções, veja os exemplos básicos e de filtro em Criar e gerenciar índices para o idioma desejado.
Observação
Se você usar mongosh
, colar o queryVector
do código de amostra no seu terminal pode levar algum tempo, dependendo da sua máquina.
Exemplos de ANN
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 $project
para fazer o seguinte:
Excluir o campo
_id
e inclua somente os camposplot
etitle
nos resultados.Adicione um campo chamado
score
que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
Salve as seguintes incorporações em um arquivo chamado
query-embeddings.js
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Carregue o arquivo em
mongosh
para usar as incorporações na sua query:load(/<path-to-file>/query-embeddings.js'); Execute a seguinte query:
1 db.embedded_movies.aggregate([ 2 { 3 "$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 $project
para fazer o seguinte:
Exclua o campo
_id
e inclua apenas os camposplot
,title
eyear
nos resultados.Adicione um campo denominado
score
que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
Salve as seguintes incorporações em um arquivo chamado
query-embeddings.js
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Carregue o arquivo em
mongosh
para usar as incorporações na sua query:load('/<path-to-file>/query-embeddings.js'); Execute a seguinte query:
1 db.embedded_movies.aggregate([ 2 { 3 "$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 camposplot
etitle
nos resultados.Adicione um campo chamado
score
que 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" : "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 camposplot
,title
eyear
nos resultados.Adicione um campo denominado
score
que 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" : { "$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 $project
para fazer o seguinte:
Excluir o campo
_id
e inclua somente os camposplot
etitle
nos resultados.Adicione um campo chamado
score
que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/v2/bson" 9 "go.mongodb.org/mongo-driver/v2/mongo" 10 "go.mongodb.org/mongo-driver/v2/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [2048]float64{ 31 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32 } 33 34 vectorSearchStage := bson.D{ 35 {"$vectorSearch", bson.D{ 36 {"index", "vector_index"}, 37 {"path", "plot_embedding_voyage_3_large"}, 38 {"queryVector", queryVector}, 39 {"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: About Time 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. 3 Score: 0.7710106372833252 4 5 Title: Retroactive 6 Plot: A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband. 7 Score: 0.760047972202301 8 9 Title: A.P.E.X. 10 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... 11 Score: 0.7576861381530762 12 13 Title: Timecop 14 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. 15 Score: 0.7576561570167542 16 17 Title: Back to the Future Part II 18 Plot: After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip. 19 Score: 0.7521393895149231 20 21 Title: Thrill Seekers 22 Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters. 23 Score: 0.7509932518005371 24 25 Title: Timerider: The Adventure of Lyle Swann 26 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, ... 27 Score: 0.7502642869949341 28 29 Title: The Time Machine 30 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. 31 Score: 0.7502503395080566 32 33 Title: The Time Traveler's Wife 34 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. 35 Score: 0.749496340751648 36 37 Title: The Final Countdown 38 Plot: A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor. 39 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 $project
para fazer o seguinte:
Exclua o campo
_id
e inclua apenas os camposplot
,title
eyear
nos resultados.Adicione um campo denominado
score
que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/v2/bson" 9 "go.mongodb.org/mongo-driver/v2/mongo" 10 "go.mongodb.org/mongo-driver/v2/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [2048]float64{ 31 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32 } 33 34 vectorSearchStage := bson.D{ 35 {"$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 }
1 Title: Chitty Chitty Bang Bang 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. 3 Year: 1968 4 Score: 0.7489712834358215 5 6 Title: The Little Prince 7 Plot: A pilot, stranded in the desert, meets a little boy who is a prince on a planet. 8 Year: 1974 9 Score: 0.7346078753471375 10 11 Title: In Search of the Castaways 12 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. 13 Year: 1962 14 Score: 0.7281590104103088 15 16 Title: Bedknobs and Broomsticks 17 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. 18 Year: 1971 19 Score: 0.720349133014679 20 21 Title: Pastoral Hide and Seek 22 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. 23 Year: 1974 24 Score: 0.7195663452148438 25 26 Title: Peter Pan 27 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 ... 28 Year: 1960 29 Score: 0.7195032238960266 30 31 Title: The Cowboys 32 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 ... 33 Year: 1972 34 Score: 0.717952311038971 35 36 Title: The 7th Voyage of Sinbad 37 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. 38 Year: 1958 39 Score: 0.7148033380508423 40 41 Title: The Red Balloon 42 Plot: A red balloon with a life of its own follows a little boy around the streets of Paris. 43 Year: 1956 44 Score: 0.7147064805030823 45 46 Title: Perri 47 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... 48 Year: 1957 49 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 camposplot
etitle
nos resultados.Adicione um campo chamado
score
que 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 = 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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 camposplot
,title
eyear
nos resultados.Adicione um campo denominado
score
que 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 java.util.Arrays.asList; 5 import static com.mongodb.client.model.Filters.*; 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.search.FieldSearchPath; 12 import com.mongodb.client.model.search.VectorSearchOptions; 13 import org.bson.Document; 14 import org.bson.conversions.Bson; 15 16 import java.util.List; 17 18 public 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 $project
para fazer o seguinte:
Excluir o campo
_id
e inclua somente os camposplot
etitle
nos resultados.Adicione um campo chamado
score
que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding_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 } 48 run().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 $project
para fazer o seguinte:
Exclua o campo
_id
e inclua apenas os camposplot
,title
eyear
nos resultados.Adicione um campo denominado
score
que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding_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 } 61 run().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 $project
para fazer o seguinte:
Excluir o campo
_id
e inclua somente os camposplot
etitle
nos resultados.Adicione um campo chamado
score
que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.
Trabalhe com uma versão executável deste exemplo como um bloco de anotações Python.
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 8 { 9 '$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 29 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 30 31 # print results 32 for 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 $project
para fazer o seguinte:
Exclua o campo
_id
e inclua apenas os camposplot
,title
eyear
nos resultados.Adicione um campo denominado
score
que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
Trabalhe com uma versão executável deste exemplo como um bloco de anotações Python.
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 8 { 9 '$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 44 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 45 46 # print results 47 for 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
.
Exemplos de ENN
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 $project
para fazer o seguinte:
Exclua o campo
_id
e inclua somente os camposplot
,title
eyear
nos resultados.Adicione um campo denominado
score
que mostra a pontuação do Atlas Vector Search dos documentos nos resultados.
Salve as seguintes incorporações em um arquivo chamado
query-embeddings.js
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Carregue o arquivo em
mongosh
para usar as incorporações na sua query:load('/<path-to-file>/query-embeddings.js'); Execute a seguinte query.
1 db.embedded_movies.aggregate([ 2 { 3 "$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 ]
1 using MongoDB.Bson; 2 using MongoDB.Bson.Serialization.Attributes; 3 using MongoDB.Bson.Serialization.Conventions; 4 using MongoDB.Driver; 5 using MongoDB.Driver.Search; 6 7 public 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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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] 47 public 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" : { "$oid" : "573a1396f29313caabce39a8" }, "plot" : "In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.", "title" : "Battle of Britain" }
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/v2/bson" 9 "go.mongodb.org/mongo-driver/v2/mongo" 10 "go.mongodb.org/mongo-driver/v2/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [2048]float64{ 31 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32 } 33 34 vectorSearchStage := bson.D{ 35 {"$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 }
1 Title: The Star 2 Plot: During WW2 the Russian Army sent a special group named "Zvezda" to fight the Nazis in their backyard. 3 Score: 0.7459506392478943 4 5 Title: P-51 Dragon Fighter 6 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. 7 Score: 0.7430292367935181 8 9 Title: Fury 10 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. 11 Score: 0.7428240776062012 12 13 Title: Memphis Belle 14 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 ... 15 Score: 0.7340154647827148 16 17 Title: The Young Lions 18 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 ... 19 Score: 0.7327103614807129 20 21 Title: White Tiger 22 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. 23 Score: 0.732368528842926 24 25 Title: The Fallen 26 Plot: The story of ordinary men during WWII as seen from three different points of View. 27 Score: 0.7301670908927917 28 29 Title: War Pigs 30 Plot: A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary. 31 Score: 0.7290490865707397 32 33 Title: The Red Baron 34 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 ... 35 Score: 0.728823184967041 36 37 Title: Battle of Britain 38 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. 39 Score: 0.7270985245704651
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 java.util.Arrays.asList; 5 6 import com.mongodb.client.MongoClient; 7 import com.mongodb.client.MongoClients; 8 import com.mongodb.client.MongoCollection; 9 import com.mongodb.client.MongoDatabase; 10 import com.mongodb.client.model.search.FieldSearchPath; 11 import com.mongodb.client.model.search.VectorSearchOptions; 12 import org.bson.Document; 13 import org.bson.conversions.Bson; 14 15 import java.util.List; 16 17 public 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}
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 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding_voyage_3_large', 22 'queryVector': [ 23 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24 ], 25 'exact': true, 26 'limit': 10 27 } 28 }, { 29 '$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 } 48 run().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.
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 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 29 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 30 31 # print results 32 for 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}