Retrieval Augmented Generation (RAG) Done Right: Document Stores

The use of structured numerical information has historically dominated data science and analytics, but with the rise of Large Language Models (LLMs), text data has become the new primary data of interest. This shift has led to the growth of document databases like Elasticsearch and MongoDB, which are now being used to store large-scale text data for mission-critical enterprise applications. The blog post discusses how to ingest text data from an Elasticsearch instance into Vectara using Airbyte, a tool that provides connectivity to popular document stores and solves common data integration problems in a single place. Once the data is ingested, it can be used with Vectara's Retrieval Augmented Generation (RAG) solution to answer questions based on the data, such as "is there a good vegetarian restaurant near Champs-Élysée?" or "which museum is best for children?" The post concludes that text data is becoming increasingly important and provides a simple way to try Vectara with your own Elasticsearch instance.

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