Introducing Scalar Quantization in Lucene
How did we introduce scalar quantization into Lucene
Scalar quantization 101
What is scalar quantization and how does it work?
Improving information retrieval in the Elastic Stack: Improved inference performance with ELSER v2
Learn about the improvements we've made to the inference performance of ELSER v2.
Improving information retrieval in the Elastic Stack: Optimizing retrieval with ELSER v2
Learn about how we're reducing retrieval costs for ELSER v2.
Generative AI architectures with transformers explained from the ground up
This long-form article explains how generative AI works, from the ground all the way up to generative transformer architectures with a focus on intuitions.
Vector search in Elasticsearch: The rationale behind the design
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.
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.
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.
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.
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.