Platform
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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Elasticsearch Vector Database - Python & PDF in S3, Claude Code (with AWS resource creation steps)
Walk through all the steps required to create a Vector Database, how to create an Amazon S3 bucket as a place to privately store original document source files, and how to use Python code to index a PDF document from the S3 bucket into a Vector Database index. Then we'll create an Elastic workflow and MCP tool that can be queried by Claude Code to give it custom context.
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Elasticsearch Vector Database - Python & PDF in S3, Claude Code
Walk through the steps required to create a Vector Database and how to use Python code to index a PDF document from an Amazon S3 bucket into a Vector Database index. Then we'll create an Elastic workflow and MCP tool that can be queried by Claude Code to give it custom context.
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Elasticsearch Vector Database - Getting Started
Use Elastic Serverless to create an Elasticsearch Vector Database project. No infrastructure to manage. We add data, generate embeddings, and get started fast with code samples you can run in the Elastic Cloud console.