Andre Luiz

A step-by-step guide to integrating Logstash with Elasticsearch for efficient data ingestion, indexing, and search.

How to ingest data to Elasticsearch through Logstash

Jeffrey Rengifo

Tomás Murúa

Learn how to use Elastic connectors to create a unified knowledge base with Playground.

Elastic Playground: Using Elastic connectors to chat with your data

Piotr Przybyl

Mastering advanced Elasticsearch integration testing: Faster, smarter, and optimized.

Advanced integration tests with real Elasticsearch

Dave Erickson

Jakob Reiter

Learn how to run a local instance of DeepSeek and connect to it from within Kibana.

Testing DeepSeek R1 locally for RAG with Ollama and Kibana

Peter Straßer

Compound words present challenges in search engines during text analysis and tokenization, as they can obscure meaningful connections between word components. Tools like the Hyphenation Decompounder Token Filter help address these issues by deconstructing compound words.

How to search languages with compound words

Explore how to use facet search in Elasticsearch to quickly narrow down options within categories.

Faceted search: Use AI to improve search scope and results

James Gallagher

Learn how to build a powerful semantic image search engine with Roboflow Inference and Elasticsearch.

Build a multimodal image retrieval system using KNN search and CLIP embeddings

Gustavo Llermaly

Second part of a two-part article to index and search OneLake data into Elastic using a Custom connector.

Indexing OneLake data into Elasticsearch - Part II

Learn to configure OneLake, consume data using Python and index documents in Elasticsearch to then run semantic searches.

Indexing OneLake data into Elasticsearch - Part 1

Han Xiang Choong

Discussing and implementing techniques which may increase RAG performance. Part 1 of 2, focusing on the data processing and ingestion component of an advanced RAG pipeline.

Advanced RAG techniques part 1: Data processing

Discussing and implementing techniques which may increase RAG performance. Part 2 of 2, focusing on querying and testing an advanced RAG pipeline.

Advanced RAG techniques part 2: Querying and testing

In this series, we'll discuss and implement techniques that may increase RAG performance. 

Advanced RAG techniques

Craig Taverner

Geospatial search in Elasticsearch Query Language (ES|QL). Elasticsearch has powerful geospatial search features, which are now coming to ES|QL for dramatically improved ease of use and OGC familiarity.

Elasticsearch geospatial search with ES|QL

How to use Kibana and the csv ingest processor to ingest geospatial data into Elasticsearch for use with search in Elasticsearch Query Language (ES|QL). Elasticsearch has powerful geospatial search features, which are now coming to ES|QL for dramatically improved ease of use and OGC familiarity. But to use these features, we need Geospatial data.

Ingest geospatial data into Elasticsearch with Kibana for use in ES|QL

This series covers how to use the new geospatial search features in ES|QL, including how to ingest geospatial data and how to use it in ES|QL queries.

Elasticsearch geospatial search

Thanos Papaoikonomou

Thomas Veasey

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 search relevance part 1 - The BEIR benchmark

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 2 - Phi-3 as relevance judge

Blog posts discussing how to think about evaluating your own search systems in the context of better understanding the BEIR benchmark. We will introduce specific tips and techniques to improve your search evaluation processes.

Evaluating search relevance

Cory Mangini

This series gives you an inside look at how we're using generative AI in customer support. Join us as we share our journey in real-time!

GenAI for Customer Support — Part 2: Building a Knowledge Library

Ian Moersen

This series gives you an inside look at how we’re using generative AI in customer support. Join us as we share our journey in designing a GenAI chatbot interface.

GenAI for Customer Support — Part 3: Designing a chat interface for chatbots... for humans

Antonio Schönmann

This series gives you an inside look at how we're using generative AI in customer support. Join us as we share our journey in real-time, focusing in this section on tuning RAG search for relevance.

GenAI for customer support — Part 4: Tuning RAG search for relevance

Andy James

This series gives you an inside look at how we're using generative AI in customer support. Join us as we share our journey in real-time, focusing in this entry on observability for the Support Assistant.

GenAI for Customer Support — Part 5: Observability

GenAI for customer support

Hemendra Singh Lodhi

Learn how to ingest data from AWS S3 using Elastic Serverless Forwarder (ESF).

How to ingest data from AWS S3 into Elastic Cloud - Part 1 : Elastic Serverless Forwarder

Learn about different options to ingest data from AWS S3 into Elastic Cloud. This blog covers how to ingest data from AWS S3 using Elastic Agent.

How to ingest data from AWS S3 into Elastic Cloud -  Part 2 : Elastic Agent

Learn about different options to ingest data from AWS S3 into Elastic Cloud. This time we will focus on Elastic S3 Connector.

How to ingest data from AWS S3 into Elastic Cloud - Part 3: Elastic S3 Connector

Learn about different ways you can ingest data from AWS S3 into Elastic Cloud.

How to ingest data from AWS S3 into Elastic Cloud

Grégoire Corbière

Quentin Herreros

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: Steps to improve search relevance

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: Benchmarking passage retrieval

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

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

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: Hybrid retrieval

This series explores steps to improve search relevance, benchmarking passage retrieval, ELSER, and hybrid retrieval.


Improving information retrieval in the Elastic Stack

Learn how to connect to OneLake and index documents into Elasticsearch. Then, take the configuration one step further by developing your own OneLake connector.

Indexing OneLake data into Elasticsearch

Learn how to write your automated tests for Elasticsearch, using mocks and Testcontainers

Testing your Java code with mocks and real Elasticsearch

Learn how to make your automated integration tests for Elasticsearch faster using various techniques for data initialization and performance improvement.

Faster integration tests with real Elasticsearch

This series demonstrates improvements for integration tests using Elasticsearch and advanced techniques to further reduce execution time in Elasticsearch integration tests.

Integration tests using Elasticsearch

David Pilato

LangChain4j (LangChain for Java) is a powerful toolset to build your RAG application in plain Java.

Introducing LangChain4j to simplify LLM integration into Java applications

LangChain4j (LangChain for Java) has Elasticsearch as an embedding store. Discover how to use it to build your RAG application in plain Java.

LangChain4j with Elasticsearch as the embedding store

Introducing LangChain4j (LangChain for Java). Discover how to use it to build your RAG application in plain Java.

Introducing LangChain4j: Building RAG apps in plain Java 

Indexing our Jira content into Elasticsearch to create a unified data source and do search with Document Level Security.

Jira connector tutorial part I

After connecting Jira to Elasticsearch, we'll now review best practices to escalate this deployment.

Jira connector tutorial part II: 6 optimization tips

Learn how to integrate Elasticsearch with Jira using Elastic’s Jira native connector and explore optimization techniques.

Jira connector tutorials

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

What is semantic reranking and how to use it?

elastic-semantic-reranker

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

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

Introducing the concept of semantic reranking and Elastic Rerank, Elastic's new semantic re-ranker model.

Semantic reranking & the Elastic Rerank model

Carly Richmond

Laurent Saint-Félix

Learn how to perform traditional text queries in Elasticsearch using the Elasticsearch Go client through a practical example.

Perform text queries with the Elasticsearch Go client

Learn how to perform vector search in Elasticsearch using the Elasticsearch Go client through a practical example.

Perform vector search in Elasticsearch with the Elasticsearch Go client

Learn how to achieve hybrid search by combining keyword and vector search using Elasticsearch and the Elasticsearch Go client.

Using hybrid search for gopher hunting with Elasticsearch and Go

This series explains how to use the Elasticsearch Go client for traditional keyword search, vector search and hybrid search.

Blogs