Learn to evaluate your search system in the context of better understanding the BEIR benchmark, with tips & techniques to improve your search evaluation processes.

Understanding the BEIR benchmark in search relevance evaluation

Structure of a BEIR dataset

Leveraging the BEIR benchmark for search relevance evaluation

Main takeaways & next steps

The BEIR benchmark & Elasticsearch search relevance evaluation

Evaluating search relevance part 1 - The BEIR benchmark

Learn how scalar quantization can be used to reduce the memory footprint of vector embeddings in Elasticsearch through an experiment.

Understanding scalar quantization in Elasticsearch

Experimentation: Evaluating scalar quantization

Overview of methodology

Results

The benefits of using scalar quantization in Elasticsearch include reducing the memory footprint of vector embeddings without significantly affecting retrieval performance.

What are the benefits of using scalar quantization in Elasticsearch?

Evaluating scalar quantization in Elasticsearch

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.

Introduction

Scalar quantization recap

Novelties introduced to scalar quantization

1. Error correcting the scalar dot product

2. Optimizing the truncation interval

Proof of principle for int4 quantization

Conclusion

Scalar quantization optimized for vector databases

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

Introduction to Int4 quantization in Lucene

How does `Int4` quantization work in Lucene

Storing and scoring the quantized vectors

Calculating the quantization error correction

Finding the optimal bucketing for int4 quantization

Speed vs. size for quantization

Speed part 2: more SIMD in int4

The end?

Int4 provides additional compression options. It reduces the quantization space to only 16 possible values (0 through 15).

What is Int4 quantization in Lucene?

Understanding Int4 scalar quantization in Lucene

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

N-gram metrics

BLEU score

ROUGE score

METEOR score

Intrinsic metrics

Perplexity (PPL)

Model-based metrics

BERTScore

BLEURT

BARTScore

UniEval: Elastic’s choice for evaluating RAG

Real-world usage of UniEval

There are various metrics used to evaluate RAG, such as: N-gram metrics (including BLEU score, ROUGE score & METEOR score), Intrinsic metrics (like PPL), Model-based metrics (such as BERTScore, BLEURT and BARTScore), and Elastic's choice: UniEval.

What metrics are commonly used to evaluate RAG?

UniEval evaluates RAG by unifying all evaluation dimensions into a Boolean Question Answering framework, allowing a single model to assess a generated text from various angles.

How does UniEval evaluate RAG?

RAG evaluation metrics: UniEval, BLEU, ROUGE & more

RAG evaluation metrics: A journey through metrics

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

Automatic byte quantization in Lucene

Scalar quantization 101

Exploring the architecture

Quantization per segment in Lucene

Quantization that grows with you

Quantization performance & numbers

Scalar quantization is a lossy compression technique. Some simple math gives significant space savings with little impact on recall.

What is scalar quantization?

Understanding scalar quantization in Lucene

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

Introduction to scalar quantization

Understanding buckets in scalar quantization

The role of statistics in scalar quantization

The role of algebra in scalar quantization

Ensuring accuracy in quantization

Quantization allows for vectors to be encoded in a lossy manner, thus reducing fidelity slightly with huge space savings.

What are the benefits of scalar quantization?

Scalar quantization takes each vector dimension and buckets them into some smaller data type.

How does scalar quantization work?

Scalar quantization 101: The basics, benefits and applications

Learn about the improvements we've made to the inference performance of ELSER v2, achieving a 60% to 120% speed increase over ELSER v1.

Improved relevance for BEIR with ELSER v2

Quantization in ELSER v2

Block layout of linear layers in ELSER v2

ElSER v2: Improved information retrieval & inference performance

Improving information retrieval in the Elastic Stack: Improved inference performance with ELSER v2

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

Retrieval cost aware training

Optimizing ELSER queries

Implementing with an Elasticsearch query

ELSER: Optimizing retrieval with ELSER v2

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

Using the Phi-3 language model as a relevance judge, with tips & techniques to improve the agreement with human-generated annotation

ML Research

Evaluating search relevance part 2 - Phi-3 as relevance judge