Solutions

Discover how we added passage vectors to Apache Lucene, the benefits of doing so, and how existing Lucene structures were used to create an efficient retrieval experience.

Gabriel Kaam, the founder of Modalova, implemented Elasticsearch to efficiently handle their vast inventory and deliver more personalized results to customers — resulting in increased conversion rates from 30% to 50% and doubling revenue in a year.

Want to find out how to apply vector search to music data? In this post, we combine information retrieval, machine learning, vector databases, and audio data to deliver new and exciting possibilities!

In this blog, we look at how ChatGPT works, and consider three approaches to build generative AI like search experiences for specific domains.

In this post, learn about the supply chain vulnerabilities of artificial intelligence large language models and how the AI retrieval techniques of search engines can be used to fight misinformation and intentional tampering of AI.

Elastic Search 8.9 brings improvements to vector search and ingestion and presents hybrid search with RRF to combine vector, keyword, and semantic techniques. Public-facing search endpoints for indices are now available with search applications beta.

Understand what an index is in Elasticsearch and how it compares to relational databases. Learn about flexible storage, efficient retrieval, advanced indexing, and search capabilities.

Learn how to leverage ESRE and generative AI in order to enable R&D organizations to interact with data effectively and make better decisions in the drug discovery process. The challenges with privacy and missing context will also be addressed.

There are different ways to implement a vector database, which have different trade-offs. In this blog, you'll learn more about how vector search has been integrated into Elastisearch and the trade-offs that we made.