Andre Luiz

A step-by-step guide to integrating Apache Kafka with Elasticsearch for efficient data ingestion, indexing, and visualization using Python, Docker Compose, and Kafka Connect.

How to ingest data to Elasticsearch through Kafka

Quentin Herreros

Thomas Veasey

Thanos Papaoikonomou

Emilia Garcia Casademont

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

Improve search results by calibrating model scoring in Elasticsearch

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

Understanding optimized scalar quantization

Kathleen DeRusso

Harness the power of query rules combined with semantic search and rerankers.


Ensuring business rules work seamlessly with semantic search

Musab Dogan

How AutoOps pinpointed and resolved high CPU usage in an Elasticsearch cluster: A step-by-step case study.

Resolving high CPU usage issues with AutoOps

Jeff Vestal

Learn how to use the Open Crawler in combination with Semantic Text to easily crawl web sites and make them semantically searchable.

Semantic search using the Open Crawler and Semantic Text

Han Xiang Choong

Discussing and implementing an agentic flow for Elastic RAG, where the LLM chooses to call an Elastic KB.

Agentic RAG with Elasticsearch & Langchain

Fernando Briano

A guide to migrate a Ruby codebase from the OpenSearch client to the Elasticsearch client.

How to migrate your Ruby app from OpenSearch to Elasticsearch

Mark Settle

George Kobar

Amena Siddiqi

Elasticsearch’s latest innovation in log management cuts the storage footprint of log data by up to 65%, enabling observability and security teams to expand visibility without exceeding their budget while keeping all data accessible and searchable.

Live log and prosper: Elasticsearch newly specialized logsdb index mode

Grégoire Corbière

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

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.

Using the Elasticsearch Go client for keyword search, vector search & hybrid search

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

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.

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

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

Piotr Przybyl

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

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.

Using Kibana to ingest geospatial data into Elasticsearch 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.