Red Hat and Elastic Fuel Retrieval Augmented Generation for GenAI Use Cases

Red Hat, Inc., the world's leading provider of open source solutions, and Elastic (NYSE: ESTC), the search artificial intelligence (AI) company, today announced an expanded collaboration to deliver next-generation search experiences supporting retrieval augmented generation (RAG) patterns using Elasticsearch as a preferred vector database solution integrated on Red Hat OpenShift AI. With this collaboration, Red Hat and Elastic are providing enterprises with the tools they need to deliver, maintain and refine RAG solutions over time on a single, consistent platform.

As organizations face the twin demands of adding AI solutions into their operations while also minimizing risk, RAG takes center stage for integrating large language models (LLM) into business applications. RAG enables IT teams to combine the benefits of LLMs with private data stores to train models with targeted, private data without modifying the underlying model itself. Strong search retrieval is key, as prompting LLMs with the correct information using private repositories at scale can be expensive. Retrieval with role-based controls helps maintain protections around sensitive data while still using it for training general-purpose LLMs.

Red Hat OpenShift AI and Elasticsearch can help organizations get the most out of RAG at both the MLOps infrastructure and application levels. Red Hat OpenShift AI provides a trusted machine learning operations (MLOps) platform to automate, build, tune, deploy and monitor models at scale. At the same time, Elasticsearch delivers a vector database and robust hybrid search solution for scaling and extracting AI responses, with advanced search and security features to make results more applicable to end users.

Red Hat supports Elasticsearch's tools for RAG and generative AI (GenAI) application developers using the Elasticsearch Relevance EngineTM (ESRETM), which includes built-in vector search and transformer models, enabling developers to build next-generation search with proprietary enterprise data. ESRE enables organizations to create deployments that are optimized for security using their proprietary structured and unstructured data, and enables developers to build semantic search and RAG applications using a variety of third-party machine learning (ML) models, as well as ecosystem tooling from providers including Cohere, LangChain and LlamaIndex.

Red Hat OpenShift AI paired with Elasticsearch allows for deeper and more comprehensive customer support, as well as further innovation and integration with Red Hat's vast ecosystem of AI partners. Successful implementations of GenAI help to build trust in AI solutions, leading to greater AI adoption and, ultimately, more user choice in the AI market.

This expansion of Red Hat's existing collaboration with Elastic exemplifies the positive impact AI can have on business applications and the broader market. By meeting enterprises where they are in their adoption of AI, Red Hat is helping them harness often underutilized data, which can be a major differentiator for organizations.

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Supporting Quotes

Steven Huels, vice president and general manager, AI Business Unit, Red Hat

"Building upon our existing collaboration with Elastic provides a value beyond RAG support for our existing customers - it shows how AI use cases continue to expand for organizations everywhere across the hybrid cloud. Broadening our partner ecosystem through our collaboration with Elastic strengthens users' power of choice on a reliable, consistent AI platform. We're pleased to bring new support for RAG patterns, a critical first step for enterprises beginning their AI journeys and building trust within the AI marketplace."

Matt Riley, general manager, Search, Elastic

"Elastic's collaboration with Red Hat is an exemplary case of advanced open code providers working together to enrich and accelerate developers' use of vector databases. Elastic's enterprise customers rely on our vector database for key business applications that require performance at scale, making a natural alignment with Red Hat OpenShift AI. Red Hat's hybrid MLOps and GenAI application development lifecycle tools will enable Elasticsearch developers to build RAG and AI-enabled search applications more easily."

In short

Red Hat and Elastic announced an expanded collaboration to deliver next-generation search experiences supporting retrieval augmented generation (RAG) patterns using Elasticsearch as a preferred vector database solution integrated on Red Hat OpenShift AI.

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