Blogs
Developer insights and practical how-to articles from our experts to inspire and empower your search experience.
Using LangExtract and Elasticsearch
Learn how to extract structured data from free-form text using LangExtract and store it as fields in Elasticsearch.
RAG with a map: Multimodal + geospatial in Elasticsearch
Combining multimodal RAG capabilities with core Elasticsearch features such as geospatial queries and lexical search.
Introducing the ES|QL query builder for the Python Elasticsearch Client
Learn how to use the ES|QL query builder, a new Python Elasticsearch client feature that makes it easier to construct ES|QL queries using a familiar Python syntax.
MCP for intelligent search
Building an intelligent search system by integrating Elastic's intelligent query layer with MCP to enhance the generative efficacy of LLMs.
Running cloud-native Elasticsearch with ECK
Learn how to provision a GKE cluster with Terraform and run the Elastic Stack on Kubernetes using ECK.
What is context engineering?
Have you heard of the new term context engineering, but aren't sure what it is? Join us as we explain what it is and how RAG with Elasticsearch can help.
Transforming data interaction: Deploying Elastic’s MCP server on Amazon Bedrock AgentCore Runtime for crafting agentic AI applications
Transform complex database queries into simple conversations by deploying Elastic's search capabilities on Amazon Bedrock AgentCore Runtime platform.
Vector search filtering: Keep it relevant
Performing vector search to find the most similar results to a query is not enough. Filtering is often needed to narrow down search results. This article explains how filtering works for vector search in Elasticsearch and Apache Lucene.
Using UBI in Elasticsearch: Creating an app with UBI and search-ui
Learn how to use UBI in Elasticsearch through a practical example. We’ll be creating an application that produces UBI events on search and click results.
Using ES|QL COMPLETION + an LLM to write a Chuck Norris fact generator in 5 minutes
Discover how to use the ES|QL COMPLETION command to turn your Elasticsearch data into creative output using an LLM in just a few lines of code.
Exploring Vertex AI with Elasticsearch
Discover how to integrate Vertex AI with Elasticsearch to create a RAG application. Follow this tutorial to configure a Gemini model and use it in Kibana's Playground.
Lighter by default: Excluding vectors from source
Elasticsearch now excludes vectors from source by default, saving space and improving performance while keeping vectors accessible when needed.