Microsoft and Elastic are thrilled to announce that Elasticsearch, the world's most downloaded vector database is an officially supported vector store and retrieval augmented search technology for Azure OpenAI Service On Your Data in public preview. The groundbreaking feature empowers you to leverage the power of OpenAI models, such as GPT-4, and incorporates the advanced capabilities of RAG (Retrieval Augmented Generation) model, directly on your data with enterprise-grade security on Azure. Read the announcement from Microsoft here.
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
Elasticsearch has native integrations to industry leading Gen AI tools and providers. Check out our webinars on going Beyond RAG Basics, or building prod-ready apps Elastic Vector Database.
To build the best search solutions for your use case, start a free cloud trial or try Elastic on your local machine now.
Related content
November 21, 2024
Elasticsearch open inference API adds support for IBM watsonx.ai Slate embedding models
How to use IBM watsonx™ Slate text embeddings when building Search AI experiences with Elasticsearch vector database.
November 15, 2024
Federated SharePoint searches with Azure OpenAI Service On your data
Using Azure OpenAI Service on your data with Elastic as vector database.
November 6, 2023
A conversational search experience for retail: Elasticsearch Relevance Engine with Google Cloud’s generative AI
This blog presents a new search experience for retailers using generative AI with Vertex AI and Elasticsearch.
October 23, 2024
Ask questions about your GitHub repository with Elasticsearch as a vector database
This blog introduces a GitHub Assistant using RAG with Elasticsearch to enable semantic code queries, providing insights into GitHub repositories, which can be extended to PRs feedback, issues handling, and production readiness reviews.
October 18, 2024
From PDF tables to insights: An alternative approach for parsing PDFs in RAG
An alternative approach to parsing PDF tables for RAG, overcoming the limitations of highly normalized formats like CSV and JSON.