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Banco de dados vetorial em escala de bilhões pronto para produção — Elasticsearch O banco de dados vetorial open source do Elasticsearch oferece uma maneira eficiente de criar, armazenar e buscar embeddings vetoriais.
Combine a busca textual e a busca vetorial para recuperação híbrida, produzindo o melhor das duas funcionalidades para ter mais relevância e precisão.
Descubra as inovações mais recentes que tornam o Elasticsearch e o Lucene a melhor escolha para bancos de dados vetoriais.
Leia o blog Aprenda a usar o Elasticsearch como um banco de dados vetorial para incorporações, potencializando a pesquisa e criando casos de uso como recuperação, geração aumentada (RAG), resumo e perguntas e respostas.
Descubra mais sobre o Search Labs A Elastic é a primeira a oferecer melhor quantização binária (BBQ), uma otimização para bancos de dados vetoriais com pesquisa vetorial mais rápida e precisa e redução de memória de 95%.
Saiba mais sobre a BBQ
Elasticsearch — o banco de dados vetorial mais implantado Copie para testar localmente em dois minutos Copy to clipboard curl -fsSL https://elastic.co/start-local | sh
Leia os documentos
Seu ponto de partida é um banco de dados vetorial Você precisa de mais de um banco de dados vetorial para ter uma experiência de busca excelente. O Elasticsearch oferece vários tipos de recuperação, modelos flexíveis de machine learning e recursos avançados de pesquisa, como agregações, filtragem e preenchimento automático.
Execute na nuvem , sem servidor , no local ou em air-gapped.
Criar endpoint de inferência
Use um modelo de machine learning e aplique-o aos seus dados no momento da ingestão.
Saiba mais sobre a API de inferência e o 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?"
}
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"
}
Execute uma pesquisa kNN para se adequar ao seu caso de uso - aplique o algoritmo ANN para obter velocidade e escala usando o índice HNSW do Lucene ou a correspondência exata.
Explore ANN e 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
}
}
}
Use filtros e lapidação para refinar a busca
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"
}
}
]
}
}
}
Combine métodos de recuperação com busca híbrida
Com a recuperação híbrida, escolha entre os métodos de recuperação que oferecem os melhores resultados para o seu caso de uso: BM25, modelo de codificador esparso treinado da Elastic (ELSER ) ou vetores densos.
Assista ao vídeo sobre pesquisa 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
}
}
}
Aplique autorização de segurança e conformidade em nível de documento
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"
}
}
Integrações de banco de dados vetorial
Por que usar um banco de dados vetorial?
Concentre a busca na intenção e no significado contextual além da correspondência de texto.
Faça a busca em todos os seus dados: texto, vetor, imagem, áudio, vídeo, geo ou dados não estruturados.
Construa cargas de trabalho de geração aumentada por recuperação para experiências de busca com GenAI usando técnicas vetoriais e híbridas.
Superset de banco de dados vetorial Escolha um banco de dados vetorial com base na experiência de busca vetorial que você quer criar.
Alguns bancos de dados vetoriais Embeddings de armazenamento
suporte completo (gratuito)
suporte completo (gratuito)
suporte completo (gratuito)
Busca híbrida (BM25 + vetores)
suporte completo (gratuito)
Filtragem, lapidações, agregações
suporte completo (gratuito)
Recurso de autocompletar na busca
suporte completo (gratuito)
Otimizada para vários tipos de dados (texto, vetor, geo)
suporte completo (gratuito)
Suporte para vários modelos de embedding
Modelo de busca semântica integrado
Pipelines de inferência de dados
Ferramentas de ingestão (rastreador da web*, conectores*, framework de API, beats, fleet, agente)
Segurança em nível de campo e documento
Ferramentas de observabilidade (Kibana)
suporte completo (gratuito)
suporte completo (gratuito)
Consultas com barras verticais - ES|QL (em breve)
suporte completo (gratuito)
Elasticsearch — em ação Veja como as organizações estão criando aplicações de busca com IA para melhorar a experiência do cliente e ajudar os usuários a encontrar exatamente o que procuram.
A Reed, a maior recrutadora do Reino Unido, reúne profissionais e empregadores usando incorporações vetoriais no Elasticsearch.
O Stack Overflow combina o poder dos especialistas humanos com a IA generativa para acelerar a recuperação de informações confiáveis das bases de conhecimento dos desenvolvedores.
A Adobe redimensiona, gerencia vários casos de uso e coloca os recursos de machine learning para trabalhar com a Elastic.
Comece a implementar a busca vetorial
Um banco de dados vetorial armazena informações como vetores, que são representações numéricas de objetos de dados, também conhecidos como embeddings vetoriais. Ele usa embeddings vetoriais para busca multimodal em um enorme conjunto de dados estruturados, não estruturados e semiestruturados, como imagens, texto, vídeos e áudio. Os bancos de dados vetoriais são construídos para gerenciar embeddings vetoriais e, portanto, oferecem uma solução completa para o gerenciamento de dados.
As embeddings vetoriais utilizam um modelo de machine learning para traduzir texto em números, permitindo que você realize buscas vetoriais. Ao converter dados em vetores, os embeddings facilitam a comparação, a pesquisa e a análise de semelhanças entre itens nesse espaço.
Um banco de dados vetorial oferece eficiência em escala, permitindo a migração perfeita de dados em ambientes locais e na nuvem e fornecendo armazenamento para incorporações vetoriais. Bancos de dados vetoriais são excelentes na pesquisa de similaridade, permitindo que você encontre facilmente itens relacionados, o que é essencial para sistemas de recomendação, pesquisa de imagens e descoberta de conteúdo. Com os recursos de busca semântica, eles vão além da simples correspondência de palavras-chave para fornecer resultados com base no significado e no contexto. Ao armazenar embeddings de vetores, eles oferecem suporte a aplicações com IA e machine learning, facilitando a implantação de modelos de NLP e de recomendação.
Sim, o Elasticsearch é o banco de dados vetorial open source mais amplamente implantado no mundo, oferecendo uma maneira eficiente de criar, armazenar e buscar incorporações de vetores em escala. Com o banco de dados vetorial empresarial da Elastic, você obtém tempos de consulta rápidos e desempenho ideal, mesmo com dados que mudam rapidamente. Construído para redimensionar, ele fornece resultados de busca relevantes e personalizados, ao mesmo tempo em que simplifica os processos de desenvolvimento.
A Elastic oferece todos os benefícios de um poderoso banco de dados vetorial, além de segurança integrada , conformidade regulatória e alta disponibilidade. Com mais de uma década de experiência em busca, a Elastic garante relevância de busca de alto nível e opções flexíveis de implantação . Como uma plataforma unificada, a Elastic minimiza a proliferação de ferramentas e a dívida técnica, ao mesmo tempo em que fornece respostas precisas com citações claras de fontes.