Chatting with your PDFs using Playground
This blog showcases a practical example of chatting with PDFs in Playground. You'll learn how to upload PDF files into Kibana and interact with them using Elastic Playground.
When hybrid search truly shines
Demonstrating when hybrid search is better than lexical or semantic search on their own.
Using Ollama and Go to build a RAG application
Building a RAG application with Go using Ollama to leverage local models.
Agentic RAG with Elasticsearch & Langchain
Discussing Agentic RAG and implementing an agentic flow where the LLM chooses to call an Elastic KB.
cRank it up! - Introducing the Elastic Rerank model (in Technical Preview)
Get started in minutes with the Elastic Rerank model: powerful semantic search capabilities, with no required reindexing, provides flexibility and control over costs; high relevance, top performance, and efficiency for text search.
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.
Exploring depth in a 'retrieve-and-rerank' pipeline
Select an optimal re-ranking depth for your model and dataset.
Using Elastic and Apple's OpenELM models for RAG systems
How to deploy and test the Apple's OpenELM models and build a RAG system using Elastic.
RAG made easy with Spring AI + Elasticsearch
Customize your AI chatbot experience with private data. Learn how to build a Retrieval-Augmented Generation (RAG) app with Spring AI and Elasticsearch.
Introducing Elastic Rerank: Elastic's new semantic re-ranker model
Learn about how Elastic's new re-ranker model was trained and how it performs.
Late chunking in Elasticsearch with Jina Embeddings v2
Using the Jina Embeddings v2 model in Elasticsearch, implementing late chunking, and exploring the pros and cons of long context embeddings models.
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.