September 8, 2025
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.
September 3, 2025
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.
August 26, 2025
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.
August 12, 2025
Beyond similar names: How Elasticsearch semantic text exceeds OpenSearch semantic field in simplicity, efficiency, and integration
Comparing Elasticsearch semantic text and OpenSearch semantic field in terms of simplicity, configurability, and efficiency.
August 8, 2025
Using Direct IO for vector searches
Using rescoring for kNN vector searches improves search recall, but can increase latency. Learn how to reduce this impact by leveraging direct IO.
July 29, 2025
Elasticsearch now with BBQ by default & ACORN for filtered vector search
Explore how Elasticsearch's vector search now delivers better results faster, and at a lower cost.
July 10, 2025
Diversifying search results with Maximum Marginal Relevance
Implementing the Maximum Marginal Relevance (MMR) algorithm with Elasticsearch and Python. This blog includes code examples for vector search reranking.
July 9, 2025
Semantic text is all that and a bag of (BBQ) chips! With configurable chunking settings and index options
Semantic text search is now customizable, with support for customizable chunking settings and index options to customize vector quantization, making semantic_text more powerful for expert use cases.
June 16, 2025
Elasticsearch open inference API adds support for IBM watsonx.ai rerank models
Exploring how to use IBM watsonx™ reranking when building search experiences in the Elasticsearch vector database.