Elastic Demo Gallery

Little examples designed to let you explore various facets of the Elastic Stack, from Kibana dashboards and Canvas workpads to Elasticsearch SQL snippets and machine learning jobs. Because trying is better than seeing.

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Observability

Observability Overview

Try out this hands-on demo to learn how to bring your logs, metrics, and traces together for complete visibility into your application environment.

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Run the Chatbot RAG App with OpenAI - using Python

Follow the step-by-step process of setting up and running the Elastic Chatbot RAG example app with OpenAI. This tour demonstrates how to use Python flask to run the app.
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AI Playground - Roll up your sleeves

Test the latest AI search capabilities with AI Playground, now in Elasticsearch. Ingest your own data or use our sample data to explore how to build RAG systems, test different LLMs from various providers like OpenAI, Amazon Bedrock, Anthropic and more.

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Elasticsearch: Ingest to Insights

From zero to start, explore this 15 minute lab to master the basics and get started quickly on Elasticsearch. Learn how to ingest and parse your data with precision, explore patterns, and create interactive visualizations.

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Run the Chatbot RAG App with OpenAI - using Docker

Follow the step-by-step process of setting up and running the Elastic Chatbot RAG example app with OpenAI. This tour demonstrates how to use Docker Compose to run the app.
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Run the Chatbot RAG App with Vertex AI - using Python

Follow the step-by-step process of setting up and running the Elastic Chatbot RAG example app with Google Cloud Vertex AI. This tour demonstrates how to use Python flask to run the app.
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Run the Chatbot RAG App with Vertex AI - using Docker

Follow the step-by-step process of setting up and running the Elastic Chatbot RAG example app with Google Cloud Vertex AI. This tour demonstrates how to use Docker Compose to run the app.
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Index Lifecycle Management

Since time-series data like logs data is continually growing over time, we can use Index Lifecycle Management to control how long the logs data should be stored for and the maximum size that the logs data should be allowed to grow. This enables you to better manage the costs associated with storing your logs data.
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