See Using OpenSearch as a Vector Database. To get the most out of this tutorial, make sure you have an understanding of: This tutorial takes approximately 30 minutes to complete. Add Airflow features like retries and alerts to your OpenSearch operations.Run dynamic queries based on upstream events in your data ecosystem or user input via Airflow params on documents and vectors stored in OpenSearch to retrieve relevant objects.Use Airflow's data-driven scheduling to run operations involving documents stored in OpenSearch based on upstream events in your data ecosystem, such as when a new model is trained or a new dataset is available.Integrating OpenSearch with Airflow allows you to: Additionally, the tool comes with a variety of plugins for use cases such as security analytics, semantic search, and neural search. OpenSearch allows you to perform complex search queries on indexed text documents. In this tutorial you'll use Airflow to create an index in OpenSearch, ingest the lyrics of the musical Hamilton into the index, and run a search query on the index to see which character most often sings a specific word. The OpenSearch Airflow provider offers modules to easily integrate OpenSearch with Airflow. It offers advanced search capabilities on large bodies of text alongside powerful machine learning plugins. OpenSearch is an open source distributed search and analytics engine based on Apache Lucene. Orchestrate OpenSearch operations with Apache Airflow
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