Category: ML Research

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Scalar quantization optimized for vector databases

Optimizing scalar quantization for the vector database use case allows us to achieve significantly better performance for the same retrieval quality at high compression ratios.

Thomas Veasey

Understanding Int4 scalar quantization in Lucene

This blog explains how int4 quantization works in Lucene, how it lines up, and the benefits of using int4 quantization.

Benjamin Trent

RAG evaluation metrics: A journey through metrics

Explore RAG evaluation metrics like BLEU score, ROUGE score, PPL, BARTScore, and more. Discover how Elastic is evaluating RAG with UniEval.

Quentin Herreros

Understanding scalar quantization in Lucene

Explore how Elastic introduced scalar quantization into Lucene, including automatic byte quantization, quantization per segment & performance insights.

Benjamin Trent

Scalar quantization 101

Understand what scalar quantization is, how it works and its benefits. This guide also covers the math behind quantization and examples.

Benjamin Trent

Improving information retrieval in the Elastic Stack: Optimizing retrieval with ELSER v2

Learn how we are reducing the retrieval costs of the Learned Sparse EncodeR (ELSER) v2.

Thomas Veasey

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, achieving a 60% to 120% speed increase over ELSER v1.

Thomas Veasey

Generative AI architectures with transformers explained from the ground up

Here's how generative AI works from the ground up, including embeddings, transformer-encoder architecture, training/fine-tuning models & more.

Aris Papadopoulos

Vector search in Elasticsearch: The rationale behind the design

In this blog, you'll learn how vector search has been integrated into Elasticsearch and the trade-offs that we made.

Adrien Grand

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.

Sean Heelan

Improving information retrieval in the Elastic Stack: Introducing Elastic Learned Sparse Encoder, our new retrieval model

Learn about the Elastic Learned Sparse Encoder (ELSER), its retrieval performance, architecture, and training process.

Thomas Veasey

Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search

Learn about the Elastic Learned Sparse Encoder (ELSER), an AI model for high relevance semantic search across domains.

Aris Papadopoulos

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