TV

Thomas Veasey

Distinguished MLE I | Data and Compute - Elasticsearch

Artigos do autor

K-means for building vector indices

30 de junho de 2025

K-means for building vector indices

We discuss optimizing k-means to efficiently create high quality vector indices

Robust Optimized Scalar Quantization

31 de maio de 2025

Robust Optimized Scalar Quantization

We discuss a sparse preconditioner to apply to vectors which results in more stable quantization performance with respect to data distribution

Speeding up merging of HNSW graphs

7 de abril de 2025

Speeding up merging of HNSW graphs

Explore the work we’ve been doing to reduce the overhead of building multiple HNSW graphs, particularly reducing the cost of merging graphs.

Improve search results by calibrating model scoring in Elasticsearch

23 de dezembro de 2024

Improve search results by calibrating model scoring in Elasticsearch

Learn how to leverage annotated data to calibrate semantic model scoring for better search results

Understanding optimized scalar quantization

19 de dezembro de 2024

Understanding optimized scalar quantization

In this post, we explain a new form of scalar quantization we've developed at Elastic that achieves state-of-the-art accuracy for binary quantization.

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

5 de dezembro de 2024

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

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

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

25 de novembro de 2024

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

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

What is semantic reranking and how to use it?

29 de outubro de 2024

What is semantic reranking and how to use it?

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

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

19 de setembro de 2024

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

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

Evaluating search relevance part 1 - The BEIR benchmark

16 de julho de 2024

Evaluating search relevance part 1 - The BEIR benchmark

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

Evaluating scalar quantization in Elasticsearch

3 de maio de 2024

Evaluating scalar quantization in Elasticsearch

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

Understanding Int4 scalar quantization in Lucene

25 de abril de 2024

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.

Scalar quantization optimized for vector databases

25 de abril de 2024

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.

Speeding Up Multi-graph Vector Search

12 de março de 2024

Speeding Up Multi-graph Vector Search

Explore multi-graph vector search in Lucene and discover how sharing information between segment searches enhances search speed.

RAG evaluation metrics: A journey through metrics

1 de dezembro de 2023

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.

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

17 de outubro de 2023

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.

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

17 de outubro de 2023

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.

Improving information retrieval in the Elastic Stack: Hybrid retrieval

20 de julho de 2023

Improving information retrieval in the Elastic Stack: Hybrid retrieval

In this blog we introduce hybrid retrieval and explore two concrete implementations in Elasticsearch. We explore improving Elastic Learned Sparse Encoder’s performance by combining it with BM25 using Reciprocal Rank Fusion and Weighted Sum of Scores.

Improving information retrieval in the Elastic Stack: Benchmarking passage retrieval

13 de julho de 2023

Improving information retrieval in the Elastic Stack: Benchmarking passage retrieval

In this blog post, we'll examine benchmark solutions to compare retrieval methods. We use a collection of data sets to benchmark BM25 against two dense models and illustrate the potential gain using fine-tuning strategies with one of those models.

Improving information retrieval in the Elastic Stack: Steps to improve search relevance

13 de julho de 2023

Improving information retrieval in the Elastic Stack: Steps to improve search relevance

In this first blog post, we will list and explain the differences between the primary building blocks available in the Elastic Stack to do information retrieval.

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

21 de junho de 2023

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.

Aggregate data faster with new the random_sampler aggregation

20 de abril de 2022

Aggregate data faster with new the random_sampler aggregation

Aggregate billions of documents in milliseconds instead of minutes with Elastic. Learn more about how the new random_sampler aggregation gives you statistically robust results at a lower cost.

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