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O Elasticsearch Relevance Engine™ (ESRE) foi projetado para alimentar aplicações de busca baseadas em inteligência artificial. Use o ESRE para aplicar busca semântica com relevância superior pronta para uso (sem adaptação de domínio), integrar com grandes modelos de linguagem (LLMs) externos, implementar busca híbrida e usar modelos transformadores de terceiros ou os seus próprios.
Use dados privados internos como contexto com as funcionalidades de modelos de IA generativa para fornecer respostas confiáveis e atualizadas para as consultas dos usuários.
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IA para todos os desenvolvedores
Ofereça funcionalidades avançadas de relevância para IA em sua aplicação com ESRE independentemente do seu nível de experiência. O ESRE tem um conjunto de recursos para ajudar você a começar ou aperfeiçoar sua experiência com AI. Você tem a flexibilidade e o controle para implantar apps de busca com machine learning e IA generativa da forma que achar melhor.
Sua primeira vez na busca semântica? Incluir a busca semântica em sua aplicação não deve requerer um nível profundo de experiência. Obtenha a melhor busca semântica da categoria pronta para usar com o modelo Elastic Learned Sparse Encoder. Com uma implantação simplificada, comece a fornecer rapidamente a busca semântica sem esforços de treinamento e manutenção de um modelo de machine learning.
Já conhece bem vetores de embeddings e de busca? Converta dados não estruturados em embeddings vetoriais e busque-os com eficiência usando a busca de vizinho mais próximo aproximado. Combine seus próprios dados específicos de domínio em janelas de contexto para melhorar a relevância dos resultados semelhantes aos humanos dos LLMs.
Use a biblioteca Python do Eland para aproveitar seu próprio modelo de machine learning treinado ou use um modelo de terceiros de um repositório público como o HuggingFace . Escolha entre uma ampla variedade de arquiteturas compatíveis que atendem às suas necessidades.
Elasticsearch Relevance Engine
Elasticsearch - Gerador multifuncional de busca vetorial Gere embeddings. Armazene, busque e gerencie vetores. Aproveite a busca semântica com o modelo de machine learning Learned Sparse Encoder da Elastic. Faça a ingestão de todos os tipos de dados. Integre com grandes modelos de linguagem em rápida evolução.
Classificação híbrida do RRF RRF (Reciprocal Rank Fusion) é um método para combinar classificações de documentos de vários sistemas de recuperação. Em um futuro próximo, o RRF oferecerá suporte para a combinação de resultados de modelos vetoriais esparsos como o BM25 e o modelo de recuperação da Elastic, gerando o melhor método de classificação zero-shot da categoria. A classificação híbrida com RRF permite ajustar os resultados da busca de vários recuperadores com menos esforço.
Elastic Learned Sparse Encoder Nosso novo modelo oferece busca semântica altamente relevante e pronta para uso, sem adaptação de domínio. Está disponível com um clique na configuração de sua aplicação de busca. O Elastic Learned Sparse Encoder expande as consultas com palavras-chave relacionadas e pontuações de relevância, para que sejam facilmente interpretáveis e estejam prontas para uso imediato.
Geração aumentada de recuperação Forneça aos LLMs informações específicas de negócios usando seus dados privados (não apenas dados treinados publicamente). Use o Elasticsearch para obter janelas de contexto de alta relevância que se baseiam nos seus dados proprietários para melhorar a saída e a relevância do LLM. Acesse a IA generativa com APIs e plugins integrados ao LLM de sua escolha.
Tenha uma experiência de busca vetorial completa — não se trata apenas de armazenar e buscar embeddings, você pode criá-los! Capture o significado e o contexto dos seus dados não estruturados, incluindo texto e imagens, com embeddings para recuperação densa. Proteja seus embeddings no nível dos documentos para garantir que os dados estejam em boas mãos.
Use seu próprio modelo transformador proprietário para o Elastic. Ou carregue modelos pré-treinados de repositórios de terceiros, como o hub de modelos da HuggingFace — com suporte para uma variedade de arquiteturas compatíveis, como BERT, BART, ELECTRA e muitas outras.
Integrações de dados e bibliotecas de ingestão Ferramentas conhecidas como Elastic Agent ou Logstash para indexar seus dados. Uma lista cada vez maior de integrações (como Confluence, S3 ou Google Drive). Conectores de banco de dados nativos (como MySQL, MongoDB). Um rastreador da web para fontes online. Para dados de apps customizados, APIs do Kibana ou crie seu próprio conector com frameworks conhecidos.
“Estou entusiasmado com os benefícios que podemos trazer para os clientes com nossos investimentos para aproveitar o Elasticsearch no RelativityOne. Estamos experimentando o ESRE agora e estamos empolgados com seu potencial para fornecer resultados de busca poderosos e ampliados por IA aos nossos clientes.”
Chris Brown, Diretor de produto, Relativity
Amostras de código
Comece a criar a busca vetorial Use uma única API para importar um modelo de embedding, gerar embeddings e escrever consultas de busca em escala usando a busca de vizinho mais próximo aproximado.
Importar um modelo da HuggingFace para o Elasticsearch
Gerar embeddings no Elasticsearch
Indexar embeddings no Elasticsearch
Busca semântica em escala
docker run - it -- rm elastic / eland \
eland_import_hub_model \
-- cloud - id $CLOUD_ID \
- u <username> - p <password> \
-- hub - model - id sentence - transformers / msmarco - MiniLM - L - 12 - v3 \
-- task - type text_embedding \
-- start
Frequently asked questions
Elasticsearch Relevance Engine is a set of features that help developers build AI search applications and includes:
Industry leading advanced relevance ranking features, including traditional keyword search with BM25, a foundation of relevant, hybrid search for all domains. Full vector database capabilities – including the ability to create embeddings, in addition to storage and retrieval of vectors. Elastic Learned Sparse Encoder – our new machine learning model for semantic search across a range of domains
Hybrid ranking (RRF) for pairing vector and textual search capabilities for optimal search relevance across a variety of domains. Support to integrate 3rd-party transformer models such as OpenAI GPT-3 and 4 via APIs A full suite of data ingestion tools such as database connectors, 3rd-party data integrations, web crawler, and APIs to create custom connectors Developer tools to build search applications across all types of data: text, images, time-series, geo, multimedia, and more. Elasticsearch is a leading search technology for websites (like ecommerce product and discovery) and internal information (such as customer success knowledge bases and enterprise search). With ESRE, we're providing a toolkit to build AI powered search experiences. Enable users to express their queries in natural language, in the form of a question or a description of the kind of information they seek. Combine this natural language capability with Generative AI to further enhance these models’ abilities with context from your own, private or proprietary data.
Yes, capabilities included with Elasticsearch Relevance Engine are designed and integrated at the _search api within Elasticsearch. Developers can use the Elastic API or familiar tools, such as Kibana, to interact with capabilities that make up Elasticsearch Relevance Engine together with Elasticsearch for a seamless experience..
Elastic Learned Sparse Encoder is a model built by Elastic for high relevance semantic search across a variety of domains. Currently, an English-only machine learning model, it captures the relationships between meanings and words for information retrieval. Interested in benchmark tests with our new retrieval model? Read this blog to learn more.
A transformer is a deep neural network architecture which serves as the basis for LLMs. Transformers consist of various components and can be composed of encoders, decoders and many “deep” neural network layers with many millions (or even billions) of parameters. They are typically trained on very large corpora of text like data on the Internet, and can be fine-tuned to perform a variety of NLP tasks. Our new retrieval model uses a transformer architecture but consists only of an encoder designed specifically for semantic search across a wide variety of domains.
All of Elasticsearch Relevance Engine’s capabilities come with Elastic Enterprise Search Platinum and Enterprise plans, as part of the 8.8 release. You can easily get started with embeddings and vector search, and try out the retrieval model model. Check out a demo of Elastic Learned Sparse Encoder's capabilities. If you have an Elasticsearch license, Elasticsearch Relevance Engine is included as part of your purchase.