Elastic Cloud on Kubernetes (ECK) is a certified offering on Red Hat OpenShift. Elastic is an IBM partner, and IBM Watsonx Assistant and Watsonx Discovery use Elastic vector search for question-answering and retrieval augmentation use cases.

With this collaboration, Elasticsearch users can benefit from Red Hat OpenShift AI, a flexible, scalable MLOps platform for building, training, testing, and serving models for AI-enabled applications.

Elasticsearch vector database for generative AI and RAG apps

Elasticsearch Relevance Engine (ESRE) is a comprehensive suite of developer tools for building generative AI and RAG applications. ESRE incorporates a vector database that stores embeddings for text, image, and video data. ESRE’s native hybrid search can effectively combine results containing text, vectors, and geospatial data, with filtering, aggregations, and document-level security.

With ESRE, developers can implement vector search and semantic search, including k-nearest neighbors (kNN) and approximate nearest neighbor (ANN) search, along with support for both built-in and third-party natural language processing (NLP) models. ESRE also seamlessly integrates with key third-party ecosystem products from providers such as Cohere, LangChain, and LlamaIndex. Elasticsearch can be self-managed or deployed with Elastic Cloud.

As part of this collaboration, users are now able to leverage ESRE capabilities by downloading Elasticsearch directly from the Red Hat Ecosystem Catalog.

What is Red Hat OpenShift AI for generative AI apps

Red Hat OpenShift AI is a hybrid MLOps platform that brings IT, data science, and app dev teams together. Designed to simplify Generative AI application development and deployment, it provides a comprehensive infrastructure stack tailored for distributed workloads. This includes training, optimizing, fine-tuning, and deploying foundational and predictive AI models. Collaborating with model builders helps provide access to a variety of pre-built models. Developers and data scientists can work together on the same platform, greatly enhancing collaboration. The platform facilitates end-to-end AI lifecycle management—from model development and training to deployment, serving, and continuous monitoring.

• Model development: Conduct exploratory data science in JupyterLab with access to core AI / ML libraries and frameworks, including TensorFlow and PyTorch using our notebook images or your own.

• Model serving & monitoring: Deploy models across on-premise or any cloud, either in a fully managed or self-managed Red Hat OpenShift footprint and centrally monitor their performance.

• Lifecycle Management: Create repeatable data science pipelines for model training and validation and integrate them with DevOps pipelines for the delivery of models across your enterprise.

• Increased capabilities and collaboration: Create projects and share them across teams. Combine Red Hat components, open-source software, and ISV-certified software.

Get started with Red Hat and Elasticsearch

To get started, just follow the installation instructions provided in the Red Hat Ecosystem Catalog, and start building your next generative AI application with RAG!

Visit Elasticsearch Labs for articles and sample notebooks on vector search, RAG, and more.

Azure OpenAI Service On Your Data makes conversational experiences come alive for your employees, customers and users. With the addition of Elasticsearch vector database and vector search technology, LLMs are enriched by your business data, and conversations deliver superior quality responses out-of-the-box. All of this adds up to helping you better understand your data, and make more informed decisions.

Build powerful conversational chat experiences, fast

Business users, such as users on e-commerce teams, product managers, and others can add documents from an Elasticsearch index to build a conversational chat experience very quickly. All it takes is a few simple steps to configure the chat experience with parameters such as message history, and you're good to go! Customers can realize benefits pretty much right away..

• Quickly roll out conversational experiences to your users, customers, or employees--backed by context from your business data

• Common use cases include offering internal knowledge search, users self-service, or chatbots that help process common business workflows

How Elasticsearch vector database works with On Your Data

The new native experience within Azure OpenAI Studio makes adding an Elastic index a simple matter. Developers can pick Elasticsearch as their chosen vector database option from the drop-down menu..

You can bring your existing Elasticsearch indexes to On Your Data—whether those indexes live on Azure or on-prem. Just select Elasticsearch as your data source, add your Elastic endpoint and API key, add an Elastic index, and you're all set!

With the Elasticsearch vector database running in the background, users get all the Elastic advantages you'd expect.

• Precision of BM25 (text) search, the semantic understanding of vector search, and the best of both worlds with hybrid search

• Document and field level security, so users can only access information they're entitled to based on their permissions

• Filters, facets, and aggregations that add a real boost to how quickly relevant context is pulled from your organisation's data, and sent to an LLM

• Choice of leveraging a range of large language model providers, including Azure OpenAI, Hugging Face, or other 3rd party models

Elastic on Microsoft Azure: a proven combination

Elastic is a proud winner of the Worldwide Microsoft Partner of the Year award for Commercial Marketplace. Elastic and Microsoft customers have been using Elasticsearch and Azure OpenAI to build futuristic search experiences, that leverage the best of AI and machine learning, today.

Ali Dalloul, VP, Azure AI Customer eXperience Engineering had this to say about the collaboration, "By harnessing the power of Azure Cloud and OpenAI, Elastic is driving the development of AI-driven solutions that redefine customer experiences. This partnership is more than just a collaboration; it's a feedback loop of innovation, benefiting customers, Elastic, and Microsoft, while empowering the broader partner ecosystem. We're delighted to offer customers Elasticsearch's strong vector database and retrieval augmentation capabilities to store and search vector embeddings for On Your Data."

"This really helps customers connect data wherever it lives. We are happy to open the spectrum of building conversational AI solutions, agnostic to location, including Elasticsearch. We are excited to see how developers build upon this integration." Adds Pavan Li, Principal Product Manager of Azure OpenAI Service On Your Data.

Elastic's clear strengths in hybrid search--combining BM25/text search with vector search for semantic relevance, was an important differentiator. With the backing of the open source Apache Lucene community, Elastic's vector database has already been widely adopted by large companies for enterprise scale use cases.

Try On Your Data with Elasticsearch vector database today

Unlock the insights with conversational AI, using Elasticsearch and Azure OpenAI On Your Data today!

• Visit Azure OpenAI Studio to build your first conversational copilot

• Connect Elasticsearch with OpenAI models

• Read more on the Microsoft Tech Community blog