Definición
knnBetaEl operador
knnBetautiliza el algoritmo Hierarchical Navigable Small Worlds para realizar una búsqueda semántica. Puedes utilizar el soporte de MongoDB Search para consultas kNN para buscar productos similares, imágenes y otro contenido.
Sintaxis
knnBeta tiene la siguiente sintaxis:
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 }
opciones
Campo | Tipo | Descripción | Necesidad |
|---|---|---|---|
| Documento | Cualquier operador de MongoDB Search para filtrar documentos según metadatos o ciertos criterios de búsqueda, lo que puede ayudar a delimitar el alcance de la búsqueda vectorial. | Opcional |
| Número | Número de vecinos más próximos a devolver. Puede especificar un número mayor que el número de documentos que se debe devolver ( | Requerido |
| string | Campo de tipo knnVector indexado para buscar. | Requerido |
| Documento | Puntuación asignada a los documentos coincidentes en los resultados. Para aprender más, consulta comportamiento de puntuación. | Opcional |
| arreglo de números | Arreglo de números de tipos BSON | Requerido |
Comportamiento
Solo puedes ejecutar kNN queries en campos que fueron indexados como tipo de MongoDB Search knnVector only.
Puede utilizar $limit después de la etapa $search para limitar el número de documentos en los resultados de la query knnBeta. Recomendamos establecer el valor de k superior al valor de $limit. Este patrón de sobrepeticion es la forma principal de equilibrar la latencia y la recuperación en tus búsquedas de ANN. Empíricamente, hemos visto que un multiplicador de 5-10 funciona bien para muchos casos de uso, pero recomendamos ajustarlo en su conjunto de datos específico.
Ejemplo
La siguiente query encuentra los 150 vecinos más cercanos a la query y limita el número restante de resultados a 50.
1 db.<collection>.aggregate({ 2 "$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 })
Rendimiento
Para mejorar el rendimiento de las query, utilice la etapa $project para seleccionar los campos que se devolverán en los resultados, a menos que necesite todos los campos en los resultados. Recomendamos excluir el campo vectorial en la $project etapa.
Puntuación
Puede usar el campo score con la $meta expresión searchScore en la $project etapa para devolver la puntuación de los documentos en los resultados.
MongoDB Search puntúa los resultados de las query kNN solo en un rango fijo de 0 a 1. Para las similitudes cosine y dotProduct, MongoDB Search normaliza la puntuación utilizando el siguiente algoritmo:
score = (1 + cosine/dot_product(v1,v2)) / 2
Limitaciones
knnBeta El operador debe ser el operador de nivel superior en tus consultas y, por lo tanto, no puedes utilizar el operador knnBeta en las siguientes situaciones:
No puede utilizar el operador knnBeta para query campos indexados mediante un vectorSearch índice de tipo. No puede utilizar la $search opción de ordenación con el operador knnBeta.
No recomendamos paginar los resultados de su búsqueda de MongoDB Search utilizando $skip y $limit después de la etapa $search.
Ejemplos
Las siguientes queries buscan en la colección de muestra sample_mflix.embedded_movies utilizando el operador knnBeta. Las consultas buscan el campo plot_embedding, que contiene incrustaciones creadas con el modelo de incrustaciones text-embedding-ada-002 de OpenAI. Si añadiste la colección de muestra a tu clúster y creaste la definición del índice de muestra para la colección, puedes cambiar a la base de datos sample_mflix y ejecutar las siguientes query en la colección.
La siguiente query busca en el campo plot_embedding utilizando el operador knnBeta para vector dimensiones y solicita hasta 5 vecinos más cercanos en los resultados. La query también especifica una $project etapa para:
Excluye el campo
_idy los camposplotytitleen los resultados.Agrega un campo llamado
scoreen los resultados.
1 db.embedded_movies.aggregate([ 2 { 3 "$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 ]
El siguiente filtro de query filtra los documentos de películas estrenadas antes o en (≤) 30 de junio de 1971 (1971-06-30), luego busca en el campo plot_embedding de los documentos filtrados vector dimensiones, y solicita hasta 5 vecinos más cercanos en los resultados. La query también especifica una $project etapa para:
Excluye el campo
_ide incluye solo los camposplot,titleyreleaseden los resultados.Agrega un campo llamado
scoreen los resultados.
1 db.embedded_movies.aggregate([ 2 { 3 "$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
MongoDB Search filtra los documentos basándose en valores del campo released menores o iguales a 1971-06-30. La query puntúa más alto el documento con el título Willy Wonka & the Chocolate Factory porque cumple con los criterios de búsqueda en la opción filter y también las dimensiones vector en la query.
Migra al Operador vectorSearch
El vectorSearch (Operador de Búsqueda de MongoDB) añade capacidades de texto analizado a tu búsqueda semántica. Para usar el vectorSearch (Operador de Búsqueda de MongoDB) en tus $search queries sobre un campo vectorial en lugar de knnBeta, completa los siguientes pasos:
1
Asegúrate de que la definición del índice utilice el tipo vector.
Si la definición del índice utiliza el tipo knnVector para indexar campos vectoriales, sustituya knnVector en su definición de índice por el tipo vector. Para obtener más información, consulta Migrar a Tipo vector.
2
Modifica tus consultas para usar el operador vectorSearch.
El vectorSearch (MongoDB Search Operator) añade capacidades de análisis de texto junto a búsqueda semántica. Para utilizar el operador vectorSearch, modifica tus consultas contra vector campos de tipo para utilizar opciones del operador vectorSearch.
Ejemplo
Reemplaza la query en el Ejemplo de filtro con lo siguiente:
1 db.embedded_movies.aggregate([ 2 { 3 "$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 otros ejemplos que demuestran opciones avanzadas de filtrado, como el emparejamiento de frases o similares, consulta Ejemplosdel operador vectorSearch.