Vincent Bosc

Learn how to use sparse vectors in Elasticsearch to boost relevance and personalize search results with minimal complexity.

Enhancing relevance with sparse vectors

Philipp Kahr

Understanding vectors has never been easier. Handcrafting vectors and figuring out various techniques to find your music friend in a heavily biased dataset.

Finding your best music friend with vectors: Spotify Wrapped, part 5

Valerio Arvizzigno

Elasticsearch will be available as the first third-party native grounding engine for Google Cloud’s Vertex AI platform and Google’s Gemini models. It enables joint users to build fully customizable GenAI experiences grounded in enterprise data, powered by the best-of-breed Search AI capabilities from Elasticsearch.

Elasticsearch vector database for native grounding in Google Cloud’s Vertex AI Platform

Jeffrey Rengifo

Learn how to create an Elasticsearch agent with CrewAI for your agent team and perform market research.

Using CrewAI with Elasticsearch

Thomas Veasey

Mayya Sharipova

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

Speeding up merging of HNSW graphs

Jonathan Simon

Learn how to configure the Elastic Chatbot RAG app using Vertex AI and run it on Google Kubernetes Engine (GKE).

Getting Started with the Elastic Chatbot RAG app using Vertex AI running on Google Kubernetes Engine

Andre Luiz

Exploring the pros and cons of automating the creation of filters and facets in a search experience using ML models vs the classical hard-coded approach.

Generating filters and facets using ML

Learn how to use models from the Amazon Nova family in Elasticsearch.

Using Amazon Nova models in Elasticsearch

Graphs are a powerful tool for detecting relationships in data. In this blog, we'll explore the relationships between artists and your music taste.

Detecting relationships in data: Spotify Wrapped, part 4

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

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

Gustavo Llermaly

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

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 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

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

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

Advanced 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

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

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

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

Semantic reranking & the Elastic Rerank model

Peter Straßer

Benjamin Trent

The article introduces the ColPali model, a late-interaction model that simplifies the process of searching complex documents with images and tables, and discusses its implementation in Elasticsearch.

Searching complex documents with ColPali - part 1

This article explores techniques for making late interaction vectors ready for large-scale production workloads, such as reducing disk space usage and improving computation efficiency.

 Scaling late interaction models in Elasticsearch - part 2

Introducing the ColPali model, its implementation in Elasticsearch, and how to scale late interaction models for large-scale vector search.


The ColPali model series

Iulia Feroli

Based on the downloadable Spotify personal history, we'll generate a custom version of "Wrapped" with the top artists, songs, and trends over the year

How to create your own Spotify Wrapped in Kibana

We will dive deeper into your Spotify data than ever before and explore connections you didn't even know existed.

Spotify Wrapped part 2: Diving deeper into the data

Anomaly detection can be a daunting task at first, but in this blog, we'll dive into it and figure out how the different jobs can help us find unusual patterns in our Spotify Wrapped data.

Anomaly detection population jobs: Spotify Wrapped, part 3 

Here's how to create your own Spotify Wrapped in Kibana and dive deep into your data.

The Spotify Wrapped series

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

Valentin Crettaz

This article is the first in a series of three that will dive into the intricacies of vector search, also known as semantic search, and how it is implemented in Elasticsearch.

A quick introduction to vector search

Learn how to set up vector search and execute k-NN searches in Elasticsearch.


How to set up vector search in Elasticsearch

Learn about hybrid search, the types of hybrid search queries Elasticsearch supports, and how to craft them.

Elasticsearch hybrid search

This series dives into the intricacies of vector search, how it is implemented in Elasticsearch, and how to run hybrid search queries in Elasticsearch.

Blogs