Category: AI

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Elasticsearch open inference API adds support for IBM watsonx.ai rerank models
Elasticsearch Labs

Elasticsearch open inference API adds support for IBM watsonx.ai rerank models

Explore how to use IBM watsonx™ reranking when building semantic search experiences in Elasticsearch.

Saikat Sarkar
Using Azure LLM Functions with Elasticsearch for smarter query experiences
Elasticsearch Labs

Using Azure LLM Functions with Elasticsearch for smarter query experiences

Explore an example real estate search app that uses Azure Gen AI LLM Functions with Elasticsearch to provide flexible hybrid search results. See step-by-step how to configure and run the example app in GitHub Codespaces.

Jonathan Simon
The current state of MCP (Model Context Protocol)
Elasticsearch Labs

The current state of MCP (Model Context Protocol)

Learn about MCP, project updates, features, security challenges, emerging use-cases, and how to tinker around with Elastic’s Elasticsearch MCP server.

JD Armada
AI-powered case deflection: build & deploy in minutes
Elasticsearch Labs

AI-powered case deflection: build & deploy in minutes

Exploring the AI Assistant Knowledge Base capabilities combined with Playground to create a self-service case deflection platform.

Tomás Murúa
Get set, build: Red Hat OpenShift AI applications powered by Elasticsearch vector database
Elasticsearch Labs

Get set, build: Red Hat OpenShift AI applications powered by Elasticsearch vector database

Learn how to use Elasticsearch with the ‘AI Generation’ Validated Pattern to rapidly deploy secure, GitOps-driven RAG applications on Red Hat OpenShift.

Tom Potoma
Spring AI and Elasticsearch as your vector database
Elasticsearch Labs

Spring AI and Elasticsearch as your vector database

Learn how to build a production-ready RAG app using Spring AI and Elasticsearch and integrate LLMs with your proprietary data using a vector database.

Josh Long
Unstructured data processing with NVIDIA NeMo Retriever, Unstructured, and Elasticsearch
Elasticsearch Labs

Unstructured data processing with NVIDIA NeMo Retriever, Unstructured, and Elasticsearch

Learn how to build a scalable data pipeline for unstructured documents using NeMo Retriever, Unstructured Platform, and Elasticsearch for RAG applications.

RAG and the value of grounding in Elasticsearch
Elasticsearch Labs

RAG and the value of grounding in Elasticsearch

Learn about RAG, grounding, and how to reduce hallucinations by connecting an LLM to your documents.

Tomás Murúa
RAG without “AG”?
Elasticsearch Labs

RAG without “AG”?

Learn how to leverage semantic search and ELSER to build a visually appealing Q&A experience, without using LLMs.

Gustavo Llermaly
​​Building a RAG workflow using LangGraph and Elasticsearch
Elasticsearch Labs

​​Building a RAG workflow using LangGraph and Elasticsearch

Learn how to configure and customize a LangGraph Retrieval Agent Template with Elasticsearch to build a RAG workflow for efficient data retrieval and AI-driven responses.

Neha Saini
Building a multi-agent recruitment search tool with AutoGen and Elasticsearch
Elasticsearch Labs

Building a multi-agent recruitment search tool with AutoGen and Elasticsearch

Learn how to integrate Microsoft's AutoGen framework with Elasticsearch and build a multi-agent system for semantic search through a practical job matching and candidate data retrieval example.

Jeffrey Rengifo
Using LlamaIndex Workflows with Elasticsearch
Elasticsearch Labs

Using LlamaIndex Workflows with Elasticsearch

Learn how to build a self-filtering Elasticsearch app using LlamaIndex Workflows with an LLM autocorrect loop and interchangeable models.

Jeffrey Rengifo

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