All Articles
Automatically updating your Elasticsearch index using Node.js and an Azure Function App
How To

Automatically updating your Elasticsearch index using Node.js and an Azure Function App

Learn how to use Node.js to keep your index current using an Azure Function App.

Jessica Garson

Elastic Cloud adds Elasticsearch Vector Database optimized profile to Microsoft Azure
Vector SearchGenerative AI

Elastic Cloud adds Elasticsearch Vector Database optimized profile to Microsoft Azure

Elasticsearch adds a new vector search optimized profile to Elastic Cloud on Microsoft Azure.

Serena Chou

Jeff Vestal

Yuvraj Gupta

ChatGPT and Elasticsearch: Creating Custom GPTs with Elastic Data
Generative AI

ChatGPT and Elasticsearch: Creating Custom GPTs with Elastic Data

Learn how to create a custom GPT using the ChatGPT interface. Use Actions to connect to your Elastic data.

Sandra Gonzales

ChatGPT and Elasticsearch: OpenAI meets private data
Generative AI

ChatGPT and Elasticsearch: OpenAI meets private data

Integrate Elasticsearch's search relevance with ChatGPT's question-answering capability to enhance your domain-specific knowledge base.

Jeff Vestal

ChatGPT and Elasticsearch: A plugin to use ChatGPT with your Elastic data
Generative AI

ChatGPT and Elasticsearch: A plugin to use ChatGPT with your Elastic data

Learn how to implement a plugin and enable ChatGPT users to extend ChatGPT with any content indexed in Elasticsearch, using the Elastic documentation.

Baha Azarmi

Chunking Large Documents via Ingest pipelines plus nested vectors equals easy passage search
Vector SearchHow To

Chunking Large Documents via Ingest pipelines plus nested vectors equals easy passage search

In this post we'll show how to easily ingest large documents and break them up into sentences via an ingest pipeline so that they can be text embedded along with nested vector support for searching large documents semantically. Generated image of a chonker.

Michael Heldebrant

Elasticsearch open inference API adds support for Cohere Embeddings
IntegrationsHow ToVector Search

Elasticsearch open inference API adds support for Cohere Embeddings

Learn more about how to use Cohere embeddings with Elastic built search experiences!

Serena Chou

Jonathan Buttner

Dave Kyle

Elasticsearch open Inference API adds support for Cohere’s Rerank 3 model
IntegrationsHow ToVector SearchGenerative AI

Elasticsearch open Inference API adds support for Cohere’s Rerank 3 model

“Elasticsearch integrates semantic reranking with Cohere’s Rerank models, with the inclusion of Rerank into our open Inference API.”

Serena Chou

Max Hniebergall

Using Elasticsearch as a vector database for Azure OpenAI On Your Data
IntegrationsHow ToVector Search

Using Elasticsearch as a vector database for Azure OpenAI On Your Data

Explore how to quickly set up and ingest data into Elasticsearch for use as a vector database with Azure OpenAI On Your Data, enabling you to chat with your private data.

Paul Oremland

Elasticsearch open inference API adds support for Azure OpenAI chat completions
IntegrationsHow ToGenerative AI

Elasticsearch open inference API adds support for Azure OpenAI chat completions

Elasticsearch open inference API adds support for Azure Open AI chat completions, providing full developer access to the Azure AI ecosystem

Tim Grein

Elasticsearch open inference API adds support for OpenAI chat completions
IntegrationsHow ToGenerative AI

Elasticsearch open inference API adds support for OpenAI chat completions

Learn how OpenAI chat completions and Elasticsearch can be used to summarize, translate or perform question & answering on any text.

Tim Grein

Designing for large scale vector search with Elasticsearch
Vector Search

Designing for large scale vector search with Elasticsearch

Part 1: High-fidelity dense vector search in Elasticsearch. Learn how we achieve a 75% memory reduction without impacting search quality.

Jim Ferenczi

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

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.

Thomas Veasey

Quentin Herreros

Valeriy Khakhutskyy

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

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

Learn about how we're reducing retrieval costs for ELSER v2.

Thomas Veasey

Quentin Herreros

Valeriy Khakhutskyy

From ES|QL to Pandas dataframes in Python
How ToIntegrations

From ES|QL to Pandas dataframes in Python

How to export ES|QL queries as Pandas dataframes in Python

Quentin Pradet

From ES|QL to PHP objects
How ToIntegrations

From ES|QL to PHP objects

How to execute and manage ES|QL queries in PHP

Enrico Zimuel

ES|QL queries to Java objects
How ToIntegrations

ES|QL queries to Java objects

How perform ES|QL queries with the Java client

Laura Trotta

Evaluating RAG: A journey through metrics
ML Research

Evaluating RAG: A journey through metrics

Learn how Elastic is evaluating RAG.

Quentin Herreros

Thomas Veasey

Thanos Papaoikonomou

How to choose between exact and approximate kNN search in Elasticsearch
How ToVector Search

How to choose between exact and approximate kNN search in Elasticsearch

Learn more about exact and approximate kNN search in Elasticsearch, and when to use each one

Carlos Delgado

Finding your puppy with Image Search
Vector Search

Finding your puppy with Image Search

Have you ever been in a situation where you found a lost puppy on the street and didn’t know if it had an owner? Learn how to do it with vector search or image search.

Alex Salgado

Elastic Cloud adds Elasticsearch Vector Database optimized instance to Google Cloud
Vector SearchGenerative AI

Elastic Cloud adds Elasticsearch Vector Database optimized instance to Google Cloud

Elasticsearch adds a new vector search optimized profile for GCP.

Serena Chou

Jeff Vestal

Yuvraj Gupta

Generative AI using Elastic and Amazon SageMaker JumpStart
Generative AIIntegrations

Generative AI using Elastic and Amazon SageMaker JumpStart

Learn how to build a generative artificial intelligence (GAI) solution with Amazon SageMaker JumpStart, Elastic, and Hugging Face open source LLMs using the sample implementation provided in this post and a data set relevant to your business.

Uday Theepireddy

Ayan Ray

How to deploy NLP: Text Embeddings and Vector Search
Vector Search

How to deploy NLP: Text Embeddings and Vector Search

Taking Text Embeddings and Vector Similarity Search as the example task, this blog describes the process for getting up and running using deep learning models for Natural Language Processing, and demonstrates vector search capability in Elasticsearch

Mayya Sharipova

Scalar Quantization Optimized for Vector Databases
ML Research

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.

Thomas Veasey

Benjamin Trent

Keeping Your Elasticsearch Index Current with Python and Google Cloud Platform Functions
How To

Keeping Your Elasticsearch Index Current with Python and Google Cloud Platform Functions

Learn how to use Python to keep your index current using Google Cloud Platform's (GCP) Cloud Functions and Cloud Scheduler.

Jessica Garson

Introducing kNN query, an expert way to do kNN search
Vector SearchHow To

Introducing kNN query, an expert way to do kNN search

How kNN query can be used and how it is different from top level kNN search

Mayya Sharipova

Benjamin Trent

Less merging and faster ingestion in Elasticsearch 8.11
Lucene

Less merging and faster ingestion in Elasticsearch 8.11

Elasticsearch 8.11 improves how it manages its indexing buffer, resulting in less segment merging.

Adrien Grand

How to get the best of lexical and AI-powered search with Elastic’s vector database
Vector Search

How to get the best of lexical and AI-powered search with Elastic’s vector database

Elastic has all you should expect from a vector database — and much more! You get the best of both worlds: traditional lexical and AI-powered search, including semantic search out of the box with Elastic’s novel Learned Sparse Encoder model.

Bernhard Suhm

Lexical and Semantic Search with Elasticsearch
Vector Search

Lexical and Semantic Search with Elasticsearch

In this blog post, you will explore various approaches to retrieving information using Elasticsearch, focusing specifically on text: lexical and semantic search.

Priscilla Parodi

How to Set Up LocalAI for GPU-Powered Text Embeddings in Air-Gapped Environments
Generative AIHow ToIntegrations

How to Set Up LocalAI for GPU-Powered Text Embeddings in Air-Gapped Environments

With LocalAI you can compute text embeddings in air-gapped environments. GPU support is available.

Valeriy Khakhutskyy

Apache Lucene 9.9, the fastest Lucene release ever
Lucene

Apache Lucene 9.9, the fastest Lucene release ever

Lucene 9.9 was released recently and brings major speedups to query evaluation

Adrien Grand

Bringing Maximum-Inner-Product into Lucene
Lucene

Bringing Maximum-Inner-Product into Lucene

How we brought maximum-inner-product into Lucene

Benjamin Trent

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

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

Deep learning has transformed how people retrieve information. We've created a retrieval model that works with a variety of text with streamlined processes to deploy it. Learn about the model's performance, its architecture, and how it was trained.

Thomas Veasey

Quentin Herreros

Accessing machine learning models in Elastic
Integrations

Accessing machine learning models in Elastic

Bring your own transformer models into Elastic to use optimized embedding models and NLP, or integrate with third-party transformer modes such as OpenAI GPT-4 via APIs to leverage more accurate, business-specific content based on private data stores.

Bernhard Suhm

Josh Devins

Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search
ML Research

Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search

Elastic Learned Sparse Encoder is an AI model for high relevance semantic search across domains. As a sparse vector model, it expands the query with terms that don't exist in the query itself, delivering superior relevance without domain adaptation.

Aris Papadopoulos

Gilad Gal

More skipping with block-max MAXSCORE
Lucene

More skipping with block-max MAXSCORE

Improve the MAXSCORE algorithm to evaluate disjuntive queries more like a conjunctive query when possible, which helps evaluate even fewer hits

Adrien Grand

Speeding Up Multi-graph Vector Search
Lucene

Speeding Up Multi-graph Vector Search

Sharing information among segment searches in multi-graph vector search allows us to achieve significant search speedups.

Mayya Sharipova

Thomas Veasey

Retrieval of originating information in multi-vector documents
Vector Search

Retrieval of originating information in multi-vector documents

Learn more on how to link original context to a multi-vector document.

Gilad Gal

Multilingual vector search with the E5 embedding model
Vector Search

Multilingual vector search with the E5 embedding model

Here's how multilingual vector search works and how to use Elasticsearch with the multilingual E5 embedding model, including examples.

Josh Devins

Evolution of the Elasticsearch .NET Client
How ToIntegrations

Evolution of the Elasticsearch .NET Client

From NEST to Elastic.Clients.Elasticsearch

Florian Bernd

Elasticsearch piped query language, ES|QL, now generally available
How To

Elasticsearch piped query language, ES|QL, now generally available

Elasticsearch Query Language (ES|QL) is GA. Simplify your data investigations with an innovative piped query language powered by a new query engine, all from a single unified UI.

Costin Leau

George Kobar

Plagiarism detection with Elasticsearch
Vector SearchHow To

Plagiarism detection with Elasticsearch

In this blog we are exploring one more use case with Natural Language Processing (NLP) models and Vector Search, plagiarism detection, beyond metadata searches.

Priscilla Parodi

Retro relevance: Balancing keyword and semantic search
Vector SearchHow To

Retro relevance: Balancing keyword and semantic search

Highlights from a Haystack 2024 talk on balancing keyword and semantic search, saving time and effort for better relevance

Kathleen DeRusso

Semantic Search as Service at a Search Center of Excellence
Vector SearchHow To

Semantic Search as Service at a Search Center of Excellence

How to implement and scale semantic search as a service for a search COE using Elastic Learned Sparse EncodeR (ELSER)

Sherry Ger

Stephen Brown

Stateless — your new state of find with Elasticsearch
ML Research

Stateless — your new state of find with Elasticsearch

Discover this future of stateless Elasticsearch. Learn how we’re investing in building a new fully cloud native architecture to push the boundaries of scale and speed.

Leaf Lin

Tim Brooks

Quin Hoxie

Improving text expansion performance using token pruning
Vector SearchHow To

Improving text expansion performance using token pruning

How to improve the performance of text expansion queries by making them more efficient without sacrificing recall

Kathleen DeRusso

Vector search in Elasticsearch: The rationale behind the design
Vector SearchML Research

Vector search in Elasticsearch: The rationale behind the design

In this blog, you'll learn how vector search has been integrated into Elasticsearch and the trade-offs that we made.

Adrien Grand

What happened in Lucene land in 2023?
Lucene

What happened in Lucene land in 2023?

2023 has been another big year for Apache Lucene, this blog reviews major milestones of 2023

Adrien Grand

Generative AI architectures with transformers explained from the ground up
ML ResearchGenerative AI

Generative AI architectures with transformers explained from the ground up

Here's how generative AI works from the ground up, including embeddings, transformer-encoder architecture, training/fine-tuning models & more.

Aris Papadopoulos

Using hybrid search for gopher hunting with Elasticsearch and Go
How ToVector Search

Using hybrid search for gopher hunting with Elasticsearch and Go

Just like animals and programming languages, search has undergone an evolution of different practices that can be difficult to pick between. In the final blog of this series, Carly Richmond and Laurent Saint-Félix combine keyword and vector search to hunt for gophers in Elasticsearch using the Go client.

Carly Richmond

Laurent Saint-Félix

Go-ing gopher hunting with Elasticsearch and Go
How To

Go-ing gopher hunting with Elasticsearch and Go

Just like animals and programming languages, search has undergone an evolution of different practices that can be difficult to pick between. Join us as we use Go to hunt for gophers in Elasticsearch using traditional keyword search.

Carly Richmond

Laurent Saint-Félix

Finding gophers with vector search in Elasticsearch and Go
How ToVector Search

Finding gophers with vector search in Elasticsearch and Go

Just like animals and programming languages, search has undergone an evolution of different practices that can be difficult to pick between. Join us on part two of our journey hunting gophers in Go with vector search in Elasticsearch.

Carly Richmond

Laurent Saint-Félix