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Base de datos vectorial a escala de mil millones lista para producción — Elasticsearch La base de datos vectorial open source de Elasticsearch ofrece una forma eficiente de crear, almacenar y buscar incrustaciones de vectores.
Combina la búsqueda de texto y la búsqueda vectorial para la recuperación híbrida, lo que tiene como resultado lo mejor de ambas capacidades para mayor relevancia y precisión.
Descubre las últimas innovaciones que hacen de Elasticsearch y Lucene la mejor opción para las bases de datos vectoriales.
Lee el blog Aprende a usar Elasticsearch como una base de datos vectorial para incrustaciones, potenciando la búsqueda y creando casos de uso como recuperación de generación aumentada (RAG), resumen, y preguntas y respuestas.
Descubre más en Search Labs Elastic es el primero en ofrecer una mejor cuantificación binaria (BBQ), una optimización para bases de datos vectoriales con una búsqueda vectorial más rápida y precisa y una reducción de memoria del 95 %.
Conoce más sobre BBQ
Elasticsearch: la base de datos vectorial más desplegada Copiar para probar localmente en dos minutos Copy to clipboard curl -fsSL https://elastic.co/start-local | sh
Lee los documentos
Una base de datos vectorial es tu punto de partida… Necesitas más que una base de datos vectorial para tener una excelente experiencia de búsqueda. Elasticsearch ofrece múltiples tipos de recuperación, modelos flexibles de machine learning y características de búsqueda avanzada como agregaciones, filtrado y autocompletar.
Se ejecuta en el cloud , sin servidor , en forma local o en entornos aislados.
Crear endpoint de inferencia
Usa un modelo de machine learning y aplícalo a tus datos en el momento de la ingesta.
Obtén más información sobre la API de inferencia y el modelo E5 .
PUT _inference / text_embedding / my - e5 - endpoint
{
"service" : "elasticsearch" ,
"service_settings" : {
"num_allocations" : 1 ,
"num_threads" : 1 ,
"model_id" : ".multilingual-e5-small"
}
}
POST _inference / my - e5 - endpoint
{
"input" : "How many adult mallard ducks fit in an american football field?"
}
Incrustaciones de índices
PUT my - index
{
"mappings" : {
"properties" : {
"my_vector" : {
"type" : "dense_vector"
},
"my_text" : {
"type" : "text"
},
"category" : {
"type" : "text"
}
}
}
}
# Index a document
PUT my - index / _doc / 2
{
"my_vector" : [
0.018743202 ,
- 0.011059246 ,
…
0.05155631 ,
0.04044561
],
"my_text" : "57,000 average sized mallard ducks fit on an American football field" ,
"category" : "weird_stats"
}
Ejecuta la búsqueda de kNN para que se ajuste a tu caso de uso: aplica el algoritmo de ANN para la velocidad y la escala usando el índice HNSW de Lucene o la coincidencia exacta.
Explora ANN y KNN .
GET / my - index / _search
{
"retriever" : {
"knn" : {
"field" : "my_vector" ,
"query_vector_builder" :{
"text_embedding" : {
"model_id" : "my-e5-endpoint" ,
"model_text" : "How many adult mallard ducks fit in an american football field??"
}
},
"k" : 10 ,
"num_candidates" : 10
}
}
}
Usa filtros y facetting para refinar la búsqueda
GET / my - index / _search
{
"retriever" : {
"knn" : {
"field" : "my_vector" ,
"query_vector_builder" :{
"text_embedding" : {
"model_id" : "my-e5-endpoint" ,
"model_text" : "How many adult mallard ducks fit in an american football field??"
}
},
"k" : 10 ,
"num_candidates" : 10 ,
"filter" : [
{
"term" : {
"category" : "weird_stats"
}
}
]
}
}
}
Combina los métodos de recuperación con la búsqueda híbrida
Con la recuperación híbrida, elige entre los métodos de recuperación que brinden los mejores resultados para tu caso de uso: BM25, el modelo de codificador disperso capacitado de Elastic (ELSER ) o vectores densos.
Mira el video sobre la búsqueda híbrida
GET / my - index / _search
{
"retriever" : {
"rrf" : {
"retrievers" : [
{
"standard" : {
"query" : {
"bool" : {
"filter" : [
{
"term" : {
"category" : "weird_stats"
}
}
]
}
}
}
},
{
"knn" : {
"field" : "vector" ,
"query_vector_builder" : {
"text_embedding" : {
"model_id" : "my-e5-endpoint" ,
"model_text" : "How many adult mallard ducks fit in an american football field??"
}
},
"k" : 10 ,
"num_candidates" : 10
}
}
],
"rank_constant" : 1 ,
"rank_window_size" : 50
}
}
}
Aplica la autorización de seguridad y cumplimiento a nivel de documentos
POST / _security / role / employee_role {
"indices" : [
{
"names" : [ "example-index" ],
"privileges" : [ "read" ],
"field_security" : {
"grant" : [ "state" , "name.first" , "name.last" ]
}
}
]
}
GET example - index / _search {
"query" : {...},
"knn" = {...},
"aggs" : {...}
}
PUT _inference / text_embedding / my - e5 - endpoint
{
"service" : "elasticsearch" ,
"service_settings" : {
"num_allocations" : 1 ,
"num_threads" : 1 ,
"model_id" : ".multilingual-e5-small"
}
}
Integraciones de bases de datos vectoriales
¿Por qué usar una base de datos vectorial?
Centra la búsqueda en la intención y el significado contextual más allá de la coincidencia de texto.
Busca en todos tus datos: texto, vectores, imagen, audio, video, geolocalización o datos no estructurados.
Crea cargas de trabajo de generación aumentada de recuperación para las experiencias de búsqueda con GenAI mediante técnicas vectoriales e híbridas.
Superconjunto de base de datos vectorial Elige una base de datos vectorial en función de la experiencia de búsqueda vectorial que quieras crear.
Algunas bases de datos vectoriales
soporte completo (gratis)
soporte completo (gratis)
soporte completo (gratis)
Búsqueda híbrida (BM25 + Vectores)
soporte completo (gratis)
Filtrado, facetas, agregaciones
soporte completo (gratis)
soporte completo (gratis)
Optimizado para múltiples tipos de datos (texto, vector, geo)
soporte completo (gratis)
Compatibilidad con varios modelos de incrustaciones
Modelo de búsqueda semántica integrado
Pipelines de inferencia de datos
Herramientas de ingesta (rastreador web*, conectores*, marco de API, beats, flota, agente)
Seguridad a nivel de documento y campo
Herramientas de observabilidad (Kibana)
soporte completo (gratis)
Buscar componentes de la UI
soporte completo (gratis)
Búsquedas canalizadas - ES|QL (próximamente)
soporte completo (gratis)
Elasticsearch en acción Mira cómo las organizaciones están creando aplicaciones de búsqueda con AI para mejorar la experiencia del cliente y ayudar a los usuarios a encontrar exactamente lo que buscan.
Reed, el mayor reclutador del Reino Unido, pone en contacto a buscadores de empleo y empleadores mediante incrustaciones de vectores en Elasticsearch.
Stack Overflow combina el poder de los expertos humanos con la AI generativa para acelerar la recuperación de información confiable de las bases de conocimiento de los desarrolladores.
Adobe escala, gestiona múltiples casos de uso y pone a trabajar las características del machine learning con Elastic.
Empieza a implementar la búsqueda de vectores
Proyectos de demostración
Una base de datos vectorial almacena información como vectores, que son representaciones numéricas de objetos de datos, también conocidos como incrustaciones de vectores. Usa incrustaciones de vectores para la búsqueda multimodal en un conjunto masivo de datos estructurados, no estructurados y semiestructurados, como imágenes, texto, videos y audio. Las bases de datos vectoriales están diseñadas para gestionar incrustaciones de vectores y, por lo tanto, ofrecen una solución completa para la gestión de datos.
Las incrustaciones de vectores aprovechan un modelo de machine learning para traducir texto en números, lo que te permite realizar búsquedas de vectores. Al convertir los datos en vectores, las incrustaciones facilitan la comparación, la búsqueda y el análisis de similitudes entre elementos de este espacio.
Una base de datos vectorial ofrece eficiencia a escala al permitir una migración de datos sin problemas entre entornos locales y el cloud, y al proporcionar almacenamiento para incrustaciones de vectores. Las bases de datos vectoriales destacan en la búsqueda de similitudes, lo que te permite encontrar fácilmente elementos relacionados, lo cual es esencial para los sistemas de recomendación, la búsqueda de imágenes y el descubrimiento de contenido. Con capacidades de búsqueda semántica, van más allá de la simple coincidencia de palabras clave para ofrecer resultados basados en el significado y el contexto. Al almacenar incrustaciones de vectores, son compatibles con aplicaciones de AI y machine learning, lo que facilita la implementación de modelos de recomendación y PNL.
Sí, Elasticsearch es la base de datos vectorial open source más desplegada del mundo, que te ofrece una manera eficiente de crear, almacenar y buscar incrustaciones de vectores a escala. Con la base de datos vectorial lista para empresas de Elastic, logras tiempos de búsqueda rápidos y un rendimiento óptimo, incluso con datos que cambian rápidamente. Diseñada para escalarse, ofrece resultados de búsqueda relevantes y personalizados, a la vez que simplifica tus procesos de desarrollo.
Elastic ofrece todos los beneficios de una potente base de datos vectorial junto con seguridad integrada , cumplimiento normativo y alta disponibilidad. Con más de una década de experiencia en búsquedas, Elastic garantiza la relevancia de las búsquedas de primer nivel y opciones de implementación flexibles . Como plataforma unificada, Elastic minimiza la expansión de herramientas y la deuda técnica, a la vez que te ofrece respuestas precisas con citas de fuentes claras.