Learn about the trade-offs using semantic reranking in search and RAG pipelines.

Retrieval

Semantic Reranking

Cross-encoders

Connection with RAG

Wrapping Up

Semantic search reranking: What it is and how to use it

What is semantic reranking and how to use it?

Learn about how Elastic's new re-ranker model was trained and how it performs

Introduction

How does it compare?

Architecture

Data sets and training

Summary

Elastic Rerank. Semantic re-ranker model

Introducing Elastic Rerank: Elastic's new semantic re-ranker model

Select an optimal re-ranking depth for your model and dataset.

The re-rankers

Oracle

Main patterns

"Pareto" curve

Discussion

"Unimodal" curve

Bad fit

Overview of patterns

Understanding scores as a function of depth

Efficiency vs effectiveness

"Latency-free" analysis

"Latency-aware" analysis

T-shirt sizing

Conclusions

Selecting a threshold

Computational budget and non-functional requirements

Relevance dataset

Semantic Reranker: Exploring depth in a 'retrieve-and-rerank' pipeline

Exploring depth in a 'retrieve-and-rerank' pipeline

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

Conclusion

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

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

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

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

Author

Thanos Papaoikonomou

Articles