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.Keep reading
Open- sourcing sysgrok — An AI assistant for analyzing, understanding, and optimizing systems
Sysgrok is an experimental proof-of-concept, intended to demonstrate how LLMs can be used to help SWEs and SREs understand systems, debug issues, and optimize performance.
Introducing Elasticsearch Relevance Engine™ — Advanced search for the AI revolution
Elasticsearch Relevance Engine™ (ESRE) powers generative AI solutions for private data sets with a vector database and machine learning models for semantic search that bring increased relevance to more search application developers.
Improving information retrieval in the Elastic Stack: Introducing Elastic Learned Sparse Encoder, our new retrieval model
Deep learning has transformed how people retrieve information. We’ve created a retrieval model that works with a variety of text with streamlined processes to deploy it. Learn about the model’s performance, its architecture, and how it was trained.
Thomas Veasey, Quentin Herreros
Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search
Elastic Learned Sparse Encoder is an AI model for high relevance semantic search across domains. As a sparse vector model, it expands the query with terms that don't exist in the query itself, delivering superior relevance without domain adaptation.
Aris Papadopoulos, Gilad Gal
Stateless — your new state of find with Elasticsearch
Discover this future of stateless Elasticsearch. Learn how we’re investing in building a new fully cloud native architecture to push the boundaries of scale and speed.
Leaf Lin, Tim Brooks and 1 other
Implementing academic papers: lessons learned from Elasticsearch and Lucene
This post shares strategies for incorporating academic papers in a software application, drawing our experiences with Elasticsearch and Lucene.