Previously, we saw the various components required to build a RAG LLM system. In that article, we discussed both how a RAG system comes together and the most common services available to fill each component of that system. In this article, we’ll focus solely on using Databricks to build out RAG. Why Databricks? We’re fans of the platform. We find it one of the most complete, powerful, and useful data platforms available in the cloud.

Leaderboard benchmarks show competitive offerings. In the rapidly evolving world of artificial intelligence, a new contender has emerged at the forefront of chatbot technology. Anthropic’s Claude 3 Opus, a large language model (LLM), has recently claimed the top spot in the Chatbot Arena, surpassing the previously dominant GPT-4. This development marks a significant shift in the AI landscape, suggesting that OpenAI may no longer hold the undisputed leadership position in the industry.

At Rearc Data, we build a wide variety of data pipelines from hard-to-process sources. These include PDF reports, web dashboards, and free text. Often, there is some pattern or visual guide that enables us to extract data cleanly. Other times, the data we want is entirely unstructured in that context, and there’s no “right” way to extract it. To illustrate this problem, let’s use a real-world use case. One of our Health and Life Sciences (HLS) datasets pulls weekly influenza data from a PDF report published by a Spanish agency.

Large language models (LLMs) have introduced a whole new world of opportunities. Suddenly, the interface between machines and human communication has broken down, and ideas that were recently untenable have become almost trivially simple. It’s an exciting time, for sure. Real success, however, depends on wrapping this new technology with software that makes it truly useful. One pattern that has arisen to make that happen is Retrieval Augmented Generation (RAG).

A picture is worth a thousand words - Fred R. Barnard In the rapidly changing world of LLMs, chatbots, and RAGs (Retrieval-Augmented Generation), most of the focus has been on plain text data. However, a wealth of information is available only in image form. What does something look like? How do elements of a diagram fit together? How is the food pyramid laid out? How do two algorithm outputs vary in visual quality?