Elasticsearch simdvec deep-dive: Walking the memory tightrope to 2x better vector throughput
A deep dive into four optimizations (cascade unrolling, batch prefetching, dim-axis unrolling, a structural refactor) that pushed Elasticsearch simdvec to 2x vector throughput by working with the CPU, not against it.
LINQ to Elasticsearch ES|QL: Write C#, query Elasticsearch
Exploring the new LINQ to Elasticsearch ES|QL provider in the Elasticsearch .NET client, which allows you to write C# code that’s automatically translated to ES|QL queries.
Building agentic applications with Elasticsearch and Microsoft’s Agent Framework
Learn how to use Microsoft Agent Framework with Elasticsearch to build an agentic application that extracts ecommerce data from Elasticsearch client libraries using ES|QL.
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.
How to use Elasticsearch Vector Store Connector for Microsoft Semantic Kernel for AI Agent development
Microsoft Semantic Kernel is a lightweight, open-source development kit that lets you easily build AI agents and integrate the latest AI models into your C#, Python, or Java codebase. With the release of Semantic Kernel Elasticsearch Vector Store Connector, developers using Semantic Kernel for building AI agents can now plugin Elasticsearch as a scalable enterprise-grade vector store while continuing to use Semantic Kernel abstractions.
NEST lifetime extended & Elastic.Clients.Elasticsearch (v8) Roadmap
Announcing the extension of the NEST (v7) lifetime and providing a high level overview of the Elastic.Clients.Elasticsearch (v8) roadmap.
Elasticsearch .NET client evolution: From NEST to Elastic.Clients.Elasticsearch
Learn about the evolution of the Elasticsearch .NET client and the transition from NEST to Elastic.Clients.Elasticsearch.