Blog
Informations pour les développeurs et articles pratiques de nos experts pour inspirer et dynamiser votre expérience de recherche
AI plagiarism: Plagiarism detection with Elasticsearch
Here's how to check for AI plagiarism using Elasticsearch, focusing on use cases with NLP models and Vector Search.
Introducing kNN Query: An expert way to do kNN search
Explore how the kNN query in Elasticsearch can be used and how it differs from top-level kNN search, including examples.
Apache Lucene 9.9, the fastest Lucene release ever
Lucene 9.9 brings major speedups to query evaluation. Here are the performance improvements observed in nightly benchmarks & optimization resources.
MAXSCORE & block-max MAXSCORE: More skipping with block-max MAXSCORE
Learn about MAXSCORE, block-max MAXSCORE & WAND. Improve the MAXSCORE algorithm to evaluate disjunctive queries more like a conjunctive query.
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.
Retrieval Augmented Generation (RAG) using Cohere Command model through Amazon Bedrock and domain data in Elasticsearch
Learn how to implement Retrieval Augmented Generation (RAG) using Cohere Command model via Amazon Bedrock & domain data in Elasticsearch.
Domain specific generative AI: pre-training, fine-tuning, and RAG
Explore strategies for integrating domain-specific knowledge into large language models (LLMs) through pre-training, fine-tuning, and RAG.
Understanding fused multiply-add (FMA) within vector similarity computations in Lucene
Learn how to use fused multiply-add (FMA) within vector similarity computations in Lucene and discover how FMA can improve performance.
Vector search & kNN implementation guide - API edition
Learn how to implement vector search and kNN using the Elasticsearch APIs via HTTP or Python.
Elastic Support Hub starts using semantic search
We transitioned our Support Hub to semantic search, a more advanced search method that understands user intent rather than relying on keywords. This transition helps provide customers with more relevant search results in Elastic's support content.
How do Elasticsearch snapshots work?
During our instructor-led “Elasticsearch Engineer” training, one of the most common questions we get while teaching about snapshots is “what is the incremental nature of snapshots?” This blog post will cover that topic in detail.
Chunking large documents via ingest pipelines plus nested vectors equals easy passage search
Learn how to chunk large documents using ingest pipelines and nested vectors in Elasticsearch for easy passage search in vector search.