Puede crear un índice de MongoDB Vector Search en un Vista para transformar documentos y colecciones, de modo que sea posible indexar parcialmente una colección, dar soporte a tipos de datos o modelos de datos incompatibles, y más.
Los siguientes ejemplos utilizan la base de datos de muestra sample_mflix.
Nota
Desambiguación
Esta página discute las vistas estándar. Para obtener más información sobre las vistas materializadas on-demand, consulte Vistas materializadas On-demand.
Para aprender sobre las diferencias entre los tipos de vistas, consulta Comparación con vistas materializadas on-demand.
Requisitos
Debes usar MongoDB 8.0 o superior.
En MongoDB v8.0+:
Usa la Interfaz de Usuario de Atlas o la API de administración de Atlas para crear índices de MongoDB Vector Search en vistas.
Ejecutar las queries de MongoDB Vector Search contra la colección fuente. Referencia el índice de MongoDB Vector Search que se creó en la Vista. Estas consultas devuelven los documentos originales tal como aparecen en la colección de origen.
En MongoDB v8.1+, también puedes:
Usar
mongoshy los métodos Driver,db.collection.createSearchIndex(),db.collection.updateSearchIndex(),db.collection.dropSearchIndex()y$listSearchIndexespara crear y gestionar índices de MongoDB Vector Search en Views.Ejecuta las consultas de búsqueda vectorial de MongoDB en la Vista.
Para editar una Vista, debes tener el rol de Administrador de Usuario y utilizar el comando collMod comando de base de datos.
Limitaciones
MongoDB Vector Search admite vistas con las siguientes etapas:
Los nombres de los índices deben ser únicos en toda una colección de fuente y todas sus vistas.
MongoDB Vector Search no admite definiciones de vista con operadores que produzcan resultados dinámicos, como la variable del sistema $$USER_ROLES y el operador de agregación $rand.
Las consultas de MongoDB Vector Search devuelven los documentos originales tal como aparecen en la colección de origen.
Ejemplo: Filtrar documentos
Para crear una vista, debe tener el privilegio de createCollection.
Puedes filtrar documentos para indexar parcialmente una colección. El siguiente ejemplo crea una vista en la colección sample_mflix.embedded_movies para asegurar que sólo se indexen los documentos que tienen un campo de incrustación de vectores.
➤ Utilice el menú desplegable Seleccione su idioma para configurar la interfaz de los ejemplos en esta página.
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Conéctese al clúster usando mongosh.
Para obtener más información, consulte Conectarse a un clúster mediante mongosh.
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En Atlas, ve a Clusters página para tu proyecto.
Si aún no se muestra, seleccione la organización que contiene su proyecto deseado en el menú Organizations de la barra de navegación.
Si aún no aparece, selecciona el proyecto deseado en el menú Projects de la barra de navegación.
En la barra lateral, haz clic en Clusters en la sección Database.
La página de clústeres se muestra.
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En Atlas, vaya a la página Search & Vector Search de su clúster.
Puedes ir a la página de búsqueda de MongoDB desde la opción Search & Vector Search o desde el Data Explorer.
Si aún no aparece, se debe seleccionar la organización que contiene el proyecto en el menú Organizations de la barra de navegación.
Si aún no se muestra, seleccione su proyecto en el menú Projects de la barra de navegación.
En la barra lateral, haz clic en Search & Vector Search en la sección Database.
Si no tiene ningún clúster, haga clic en Create cluster para crear uno. Para obtener más información, consulte Crear un clúster.
Si el proyecto tiene varios clústeres, se debe seleccionar el clúster que se desea usar en el menú desplegable Select cluster y luego se debe hacer clic en Go to Search.
Aparece la página de Búsqueda y Búsqueda Vectorial.
Si aún no aparece, se debe seleccionar la organización que contiene el proyecto en el menú Organizations de la barra de navegación.
Si aún no se muestra, seleccione su proyecto en el menú Projects de la barra de navegación.
En la barra lateral, haz clic en Data Explorer en la sección Database.
Expanda la base de datos y seleccione la colección.
Haga clic en la pestaña Indexes para la colección.
Haga clic en el enlace Search and Vector Search en el banner.
Aparece la página de Búsqueda y Búsqueda Vectorial.
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Se debe iniciar la configuración del índice.
Realiza las siguientes selecciones en la página y luego haz clic en Next.
Search Type | Seleccione el tipo de índice MongoDB Vector Search. |
Index Name and Data Source | Especifique la siguiente información:
|
Configuration Method | For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor. |
IMPORTANTE:
Tu índice de búsqueda de MongoDB se llama default por defecto. Si mantienes este nombre, tu índice será el índice de búsqueda predeterminado para cualquier consulta de búsqueda de MongoDB que no especifique una index opción diferente en sus operadores. Si creas varios índices, te recomendamos que mantengas una convención de nomenclatura coherente y descriptiva en todos ellos.
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Verifique el estado.
El índice recién creado se muestra en la página Search & Vector Search. Mientras se crea el índice, el campo Status muestra Build in Progress. Cuando finaliza la creación del índice, el campo Status muestra Active.
Nota
Las colecciones más grandes tardan más en indexarse. Se recibirá una notificación por correo electrónico cuando el índice haya terminado de construirse.
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Ejecuta una query en el índice parcial embeddingsIndex.
Nota
El siguiente ejemplo consulta el índice embeddingsIndex ejecutando el comando .aggregate en la Vista denominada moviesWithEmbeddings. Si tu clúster está ejecutando MongoDB v8.0, debes query la colección fuente (por ejemplo, embedded_movies) utilizando el índice en la Vista. Actualiza a MongoDB v8.1+ para query la Vista directamente.
Guarde las siguientes incrustaciones en un archivo llamado
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Este archivo contiene las incrustaciones para el query término
time travel.En
mongosh, carga el archivo enmongoshpara utilizar las incrustaciones en tu query:load('/<path-to-file>/query-embeddings.js'); Ejecute la siguiente query:
1 use sample_mflix 2 db.moviesWithEmbeddings.aggregate([ 3 { 4 "$vectorSearch": { 5 "index": "embeddingsIndex", 6 "path": "plot_embedding_voyage_3_large", 7 "queryVector": TIME_TRAVEL_EMBEDDING, 8 "numCandidates": 100, 9 "limit": 10 10 } 11 }, 12 { 13 "$project": { 14 "_id": 0, 15 "plot": 1, 16 "title": 1, 17 } 18 } 19 ]) [ { 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' }, { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', title: 'Retroactive' }, { 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.' }, { 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' }, { 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' }, { 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' }, { 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' }, { 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' }, { 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" }, { 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' } ]
1
Conéctate a tu implementación de MongoDB usando mongosh.
Para obtener más información, consulte Conectarse a un clúster mediante mongosh.
3
4
5
Ejecuta una query en el índice parcial embeddingsIndex.
Nota
El siguiente ejemplo consulta el índice embeddingsIndex ejecutando el comando .aggregate en la Vista denominada moviesWithEmbeddings. Si tu clúster está ejecutando MongoDB v8.0, debes query la colección fuente (por ejemplo, embedded_movies) utilizando el índice en la Vista. Actualiza a MongoDB v8.1+ para query la Vista directamente.
Guarde las siguientes incrustaciones en un archivo llamado
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Este archivo contiene las incrustaciones para el query término
time travel.En
mongosh, carga el archivo enmongoshpara utilizar las incrustaciones en tu query:load('/<path-to-file>/query-embeddings.js'); Ejecute la siguiente query:
1 use sample_mflix 2 db.moviesWithEmbeddings.aggregate([ 3 { 4 "$vectorSearch": { 5 "index": "embeddingsIndex", 6 "path": "plot_embedding_voyage_3_large", 7 "queryVector": TIME_TRAVEL_EMBEDDING, 8 "numCandidates": 100, 9 "limit": 10 10 } 11 }, 12 { 13 "$project": { 14 "_id": 0, 15 "plot": 1, 16 "title": 1, 17 } 18 } 19 ]) [ { 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' }, { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', title: 'Retroactive' }, { 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.' }, { 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' }, { 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' }, { 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' }, { 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' }, { 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' }, { 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" }, { 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' } ]
Consideraciones sobre el rendimiento
Las transformaciones de vistas altamente complejas pueden aumentar el tiempo de indexación y query. Esto se debe a que el mongod debe leer la definición de vista cuando filtra y transforma las entradas del oplog durante la indexación (sincronización inicial y replicación en estado estable), y cuando aplica esas transformaciones a los documentos devueltos en tiempo de query.
Considera crear una vista materializada para evitar una carga extra de replicación en Atlas. También puedes query la colección de origen directamente para evitar la latencia de queries que se produce con la transformación de la vista.
Solucionar problemas
Los índices cambian a FAILED
Los índices cambian al estado FAILED en los siguientes escenarios:
Creas un índice en una Vista que es incompatible con MongoDB Vector Search.
Editaste una vista de forma que no cumpla los requisitos de compatibilidad con MongoDB Vector Search.
Remover o cambiar la colección fuente de una vista.
Por ejemplo, si se crea una vista sobre otra vista y se cambia la fuente de la vista superior a otra colección.
Nota
Esta limitación también se aplica si una Vista es descendiente de otras Vistas. Por ejemplo, no se puede cambiar o remover la colección de origen de la que provienen todos los descendientes.
Los índices cambian a STALE
Los índices cambian al estado STALE en los siguientes escenarios:
Advertencia
Si el pipeline de agregación definido en tu Vista es incompatible con los documentos de la colección, la replicación de búsqueda falla. Por ejemplo, si una expresión $toDouble opera sobre un campo del documento que contiene un arreglo, la replicación falla. Asegúrate de que tu Vista funcione con todos los documentos de la colección sin errores.
Si la definición de la vista provoca un fallo de agregación mientras un índice está
READY, el índice se convierte enSTALE. El índice volverá aREADYdespués de que resuelvas el documento o cambies la definición de vista para que no falle más. CuandoSTALE, el índice sigue siendo consultable. Si el índice se cae del oplog, se activa una reconstrucción del índice.Si la definición de la Vista provoca un fallo en el pipeline de agregación mientras el índice es
BUILDING, la creación de índices queda atascada hasta que soluciones el documento. El índice volverá aREADYdespués de que resuelvas el documento o cambies la definición de vista para que no falle más.
Puede consultar el estado de los índices en la interfaz de usuario de Atlas, en la página de detalles del estado del índice.
Error: $search solo es válido como la primera etapa en un pipeline
Este error aparece cuando se hace una query a una vista usando una versión de MongoDB anterior a 8.1.
Si utiliza una versión de MongoDB anterior a la 8.0, le recomendamos que actualice a la 8.1+ para query la vista directamente. Puedes actualizar a 8.0 para query la colección de origen.
Si usas MongoDB 8.0, deberás query el índice de visualización en la colección de origen. Por ejemplo, ejecuta
.aggregate()en la colección en lugar de en la vista.
Proceso de índice
Cuando se crea un índice de MongoDB Vector Search en una vista, el proceso mongot realiza las mismas tareas que cuando se crea un índice de MongoDB Vector Search en una colección normal. El proceso mongot:
Crea índices de búsqueda vectorial MongoDB basados en las reglas de la definición de índices para la colección.
Supervisa los change streams para el estado actual de los documentos e índices de las colecciones para las cuales definiste los índices de MongoDB Vector Search.
Procesa las queries de búsqueda vectorial en MongoDB Vector Search y devuelve los identificadores de los documentos y otros metadatos de búsqueda de los documentos coincidentes a
mongod, que luego realiza una búsqueda completa de documentos y devuelve los resultados al cliente.
Cuando se crea un índice de MongoDB Vector Search en una Vista, la definición de la Vista se aplica durante los pasos 1 y 2, y los documentos transformados se indexan según la definición del índice de búsqueda, y luego se almacenan en disco.
Obtén más información
Para obtener más información sobre las Vistas, consulte Vistas.
Para crear un índice de búsqueda de MongoDB en una vista, consulte la sección "Usar vistas con la búsqueda de MongoDB".