Definição
knnBetaO operador
knnBetausa o algoritmo Hierarchical Navigable Small Worlds para realizar a pesquisa semântica. Você pode usar o suporte do MongoDB Pesquisa para kNN queries para pesquisar produtos, imagens e outros conteúdos semelhantes.
Sintaxe
knnBeta tem a seguinte sintaxe:
1 { 2 $search: { 3 "index": "<index name>", // optional, defaults to "default" 4 "knnBeta": { 5 "vector": [<array-of-numbers>], 6 "path": "<field-to-search>", 7 "filter": {<filter-specification>}, 8 "k": <number>, 9 "score": {<options>} 10 } 11 } 12 }
Opções
Campo | Tipo | Descrição | necessidade |
|---|---|---|---|
| documento | Qualquer operador do MongoDB Search para filtrar os documentos com base em metadados ou determinados critérios de pesquisa, que podem ajudar a reduzir o escopo da pesquisa vetorial. | Opcional |
| número | Number of nearest neighbors to return. You can specify a number higher than the number of documents to return ( | Obrigatório |
| string | Campo de tipo knnVector indexado a ser pesquisado. | Obrigatório |
| documento | Score assigned to matching documents in the results. To learn more, see scoring behavior. | Opcional |
| array de números | Array de números do tipo BSON | Obrigatório |
Comportamento
Você pode executar consultas kNN em campos que foram indexados como MongoDB Search tipo knnVector somente.
You can use $limit after the $search stage to limit the number of documents in the knnBeta query results. We recommend setting the value for k higher than the value for $limit. This overrequest pattern is the main way to trade off latency and recall in your approximate nearest neighbor searches. Empirically, we have seen a multiplier of 5-10 work well for many use cases, but we recommend tuning this on your specific dataset.
Exemplo
A query seguinte encontra 150 vizinhos mais próximos da query e limita o número restante de resultados para 50.
1 db.<collection>.aggregate({ 2 "$search": { 3 "knnBeta": { 4 "vector": <array-of-numbers-to-search>, 5 "path": <indexed-field-to-search>, 6 "k": 150 7 } 8 } 9 }, 10 { 11 "$limit": 50 12 })
Desempenho
To improve query performance, use the $project stage to select the fields to return in the results, unless you need all the fields in the results. We recommend excluding the vector field in the $project stage.
Pontuação
You can use the score field with the $meta expression searchScore in the $project stage to return the score for the documents in the results.
O MongoDB Search pontua os resultados para consultas kNN em uma faixa fixa de 0 a 1 apenas. Para cosine e dotProduct similaridades, o MongoDB Search normaliza a pontuação utilizando o seguinte algoritmo:
score = (1 + cosine/dot_product(v1,v2)) / 2
Limitações
knnBeta o operador deve ser o operador de nível superior em suas queries e, portanto, você não pode usar o operador knnBeta dentro do seguinte:
You can't use the knnBeta operator to query fields indexed using a vectorSearch type index. You can't use the $search sort option with the knnBeta operator.
We don't recommend paginating your MongoDB Search results using $skip and $limit after the $search stage.
Exemplos
The following queries search the sample sample_mflix.embedded_movies collection using the knnBeta operator. The queries search the plot_embedding field, which contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. If you added the sample collection to your cluster and created the sample index definition for the collection, you can switch to the sample_mflix database and run the following queries against the collection.
The following query searches the plot_embedding field using the knnBeta operator for vector dimensions and requests up to 5 nearest neighbors in the results. The query also specifies a $project stage to:
Exclua o campo
_ide inclua os camposplotetitlenos resultados.Adicione um campo denominado
scorenos resultados.
1 db.embedded_movies.aggregate([ 2 { 3 "$search": { 4 "index": "default", 5 "knnBeta": { 6 "vector": 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7 "path": "plot_embedding", 8 "k": 5 9 } 10 } 11 }, 12 { 13 "$project": { 14 "_id": 0, 15 "plot": 1, 16 "title": 1, 17 "score": { $meta: "searchScore" } 18 } 19 } 20 ])
1 [ 2 { 3 plot: 'When two girls move to the country to be near their ailing mother, they have adventures with the wonderous forest spirits who live nearby.', 4 title: 'My Neighbor Totoro', 5 score: 0.9999986886978149 6 }, 7 { 8 plot: "To escape a war, a girl flees to a remote farmhouse and becomes part of an expansive family's unusual, perhaps even supernatural lifestyle.", 9 title: 'Black Moon', 10 score: 0.8282386660575867 11 }, 12 { 13 plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.', 14 title: 'Forest Warrior', 15 score: 0.8196926712989807 16 }, 17 { 18 plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.', 19 title: 'Forest Warrior', 20 score: 0.8196901082992554 21 }, 22 { 23 plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.', 24 title: 'Coraline', 25 score: 0.815956711769104 26 } 27 ]
The following query filters the documents for movies released before or on (≤) 30 June, 1971 (1971-06-30), then searches the plot_embedding field in the filtered documents for vector dimensions, and requests up to 5 nearest neighbors in the results. The query also specifies a $project stage to:
Exclua o campo
_ide inclua apenas os camposplot,titleereleasednos resultados.Adicione um campo denominado
scorenos resultados.
1 db.embedded_movies.aggregate([ 2 { 3 "$search": { 4 "index": "default", 5 "knnBeta": { 6 "vector": 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7 "path": "plot_embedding", 8 "filter": { 9 "range": { 10 "lte": ISODate("1971-06-30 00:27:59.177000000"), 11 "path": "released" 12 } 13 }, 14 "k": 5 15 } 16 } 17 }, 18 { 19 "$project": { 20 "_id": 0, 21 "title": 1, 22 "plot": 1, 23 "released": 1, 24 "score": { $meta: "searchScore" } 25 } 26 } 27 ])
1 [ 2 { 3 plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.', 4 title: 'Willy Wonka & the Chocolate Factory', 5 released: ISODate("1971-06-30T00:00:00.000Z"), 6 score: 0.9999985694885254 7 }, 8 { 9 plot: 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.', 10 title: 'Chitty Chitty Bang Bang', 11 released: ISODate("1968-12-18T00:00:00.000Z"), 12 score: 0.7576897740364075 13 }, 14 { 15 plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.', 16 title: 'The Red Balloon', 17 released: ISODate("1957-03-11T00:00:00.000Z"), 18 score: 0.7515948414802551 19 }, 20 { 21 plot: 'While hearing the story of "Gulliver's Travels", a young boy dreams he is the title character on the island of Lilliput .', 22 title: 'The New Gulliver', 23 released: ISODate("1935-11-03T00:00:00.000Z"), 24 score: 0.7495509386062622 25 }, 26 { 27 plot: 'A living puppet, with the help of a cricket as his conscience, must prove himself worthy to become a real boy.', 28 title: 'Pinocchio', 29 released: ISODate("1940-02-23T00:00:00.000Z"), 30 score: 0.7472590804100037 31 } 32 ] 33
O MongoDB Search usa um filtro para documentos com base nos valores do campo released menores ou iguais a 1971-06-30. A query pontua o documento com o título Willy Wonka & the Chocolate Factory mais alto porque ele corresponde aos critérios de pesquisa na opção filter e também às dimensões vector na query.
Migrar para o operador vectorSearch
O vectorSearch (Operador de Pesquisa MongoDB) adiciona funcionalidade de texto analisado à sua pesquisa semântica. Para usar o vectorSearch (Operador de Pesquisa MongoDB) em suas queries de $search em um campo de vetor em vez de knnBeta, complete as seguintes etapas:
1
Certifique-se de que sua definição de índice use o tipo vector.
Se a sua definição de índice utilizar o tipo knnVector para indexar campos de vetor, substitua knnVector na sua definição de índice pelo tipo vector. Para aprender mais, consulte Migrar para o tipo vector.
2
Modifique suas queries para usar o operador vectorSearch.
O vectorSearch (operador de Pesquisa MongoDB) adiciona funcionalidades de texto analisadas juntamente com a pesquisa semântica. Para usar o operador vectorSearch, modifique suas consultas em relação aos campos de tipo vector para usar as opções do operador vectorSearch.
Exemplo
Substitua a query no Exemplo de filtro pelo seguinte:
1 db.embedded_movies.aggregate([ 2 { 3 "$search": { 4 "index": "default", 5 "vectorSearch": { 6 "filter": { 7 "range": { 8 "lte": ISODate("1971-06-30 00:27:59.177000000"), 9 "path": "released" 10 } 11 }, 12 "path": "plot_embedding", 13 "queryVector": 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14 "limit": 10, 15 "numCandidates": 1000 16 } 17 } 18 }, 19 { 20 "$project": { 21 "_id": 0, 22 "title": 1, 23 "plot": 1, 24 "released": 1, 25 "score": { $meta: "searchScore" } 26 } 27 } 28 ])
Para outros exemplos que demonstram opções avançadas de filtro, como correspondência difusa ou de frase, consulte vectorSearch operador Exemplos.