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Hybrid search revisited: introducing the linear retriever in Elasticsearch!
Elasticsearch Labs

Hybrid search revisited: introducing the linear retriever in Elasticsearch!

Discover how the linear retriever enhances hybrid search by leveraging weighted scores and MinMax normalization for more precise and consistent rankings, and learn how to use it.

Panagiotis Bailis
Creating judgement lists with Quepid
Elasticsearch Labs

Creating judgement lists with Quepid

Learn how to create judgement lists in Quepid using a collaborative human rater process and use the benchmarks to tune your relevance.

Daniel Wrigley
Displaying fields in an Elasticsearch index
Elasticsearch Labs

Displaying fields in an Elasticsearch index

Exploring techniques for displaying fields in an Elasticsearch index.

Kofi Bartlett
How we rebuilt autocomplete for ES|QL
Elasticsearch Labs

How we rebuilt autocomplete for ES|QL

Discover how we rearchitected an autocomplete engine for ES|QL to support language evolution instead of resisting it.

Drew Tate
First to hybrid search: with Elasticsearch and Semantic Kernel
Elasticsearch Labs

First to hybrid search: with Elasticsearch and Semantic Kernel

Learn how use hybrid search and the Semantic Kernel connector with Elasticsearch to improve retrieval results.

Enrico Zimuel
Get set, build: Red Hat OpenShift AI applications powered by Elasticsearch vector database
Elasticsearch Labs

Get set, build: Red Hat OpenShift AI applications powered by Elasticsearch vector database

Learn how to use Elasticsearch with the ‘AI Generation’ Validated Pattern to rapidly deploy secure, GitOps-driven RAG applications on Red Hat OpenShift.

Tom Potoma
Cracking the code on search quality: The role of judgment lists
Elasticsearch Labs

Cracking the code on search quality: The role of judgment lists

Explore why a judgment list is essential, the different types of judgments, and the key factors that define search quality.

Daniel Wrigley
Spring AI and Elasticsearch as your vector database
Elasticsearch Labs

Spring AI and Elasticsearch as your vector database

Learn how to build a production-ready RAG app using Spring AI and Elasticsearch and integrate LLMs with your proprietary data using a vector database.

Josh Long
Elasticsearch in JavaScript the proper way, part II
Elasticsearch Labs

Elasticsearch in JavaScript the proper way, part II

Learn about production best practices and how to run the Elasticsearch Node.js client in Serverless environments to reduce coding errors.

Jeffrey Rengifo
How to optimize Elasticsearch disk space and usage
Elasticsearch Labs

How to optimize Elasticsearch disk space and usage

Learn how to prevent and handle cases when the Elasticsearch disk is too full (overutilization) and when the disk capacity is underutilized to optimize cluster costs.

Kofi Bartlett
Elasticsearch in JavaScript the proper way, part I
Elasticsearch Labs

Elasticsearch in JavaScript the proper way, part I

Explaining how to create a production-ready Elasticsearch backend in JavaScript. Explore how to use Elasticsearch with JavaScript to create a server with different search endpoints to query Elasticsearch documents following client/server best practices.

Jeffrey Rengifo
How to configure the number of replicas in an Elasticsearch index
Elasticsearch Labs

How to configure the number of replicas in an Elasticsearch index

Learn how to configure the number_of_replicas in an Elasticsearch index to improve search performance and provide resilience to node failure.

Kofi Bartlett