December 16, 2024
Agentic RAG with Elasticsearch & Langchain
Discussing and implementing an agentic flow for Elastic RAG, where the LLM chooses to call an Elastic KB.
December 6, 2024
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.
November 28, 2024
Using Elastic and Apple's OpenELM models for RAG systems
How to deploy and test the new Apple Models and build a RAG system using Elastic.
November 26, 2024
RAG made easy with Spring AI + Elasticsearch
Customizing your AI chatbot experience with private data
November 22, 2024
Late chunking in Elasticsearch with Jina Embeddings v2
Using the Jina Embeddings v2 model in Elasticsearch and exploring the pros and cons of long context embeddings models.
November 21, 2024
Elasticsearch open inference API adds support for IBM watsonx.ai Slate embedding models
How to use IBM watsonx™ Slate text embeddings when building Search AI experiences with Elasticsearch vector database.
November 15, 2024
Federated SharePoint searches with Azure OpenAI Service On your data
Using Azure OpenAI Service on your data with Elastic as vector database.
October 23, 2024
Ask questions about your GitHub repository with Elasticsearch as a vector database
This blog introduces a GitHub Assistant using RAG with Elasticsearch to enable semantic code queries, providing insights into GitHub repositories, which can be extended to PRs feedback, issues handling, and production readiness reviews.
October 18, 2024
From pdf tables to insights: An alternative approach for parsing pdfs in RAG
An alternative approach to parsing pdf tables for RAG, overcoming the limitations of highly normalized formats like CSV and JSON.