Retrieval augmented generation (RAG) can be used to personalize LLMs interactions by injecting a prompt to the user query. Vector indexes have been the most common way people index and retrieve documents to build RAG pipelines.

In this talk we are going to explore how to use Elastic in combination with Hopsworks feature store to augment document-based RAG pipelines with real-time structured data from the feature store. We'll do so by building a LLM based application to plan my commute on the Stockholm commuter rail.

‍Register here

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