RAG: Seamlessly Integrating Context from Multiple Sources into Delta Tables in Databricks

In a data-driven world where essential information is scattered across diverse platforms, Unstructured Platform provides a solution by standardizing data preprocessing for seamless integration into Retrieval-Augmented Generation (RAG) applications. This tutorial demonstrates how to connect to data sources like Amazon S3 and Google Drive, preprocess documents into RAG-ready formats, and store them in a Delta Table in Databricks. Using annual 10-K SEC filings from companies like Walmart, Kroger, and Costco, the guide outlines steps to create source connectors, set up a Delta Table, and configure a data processing workflow involving partitioning, enrichment, chunking, and embedding. It also covers building a vector search index in Databricks for effective retrieval, ultimately enabling the construction of a RAG application using LangChain. The tutorial emphasizes the platform's capability to streamline data handling from multiple sources, facilitating enhanced data accessibility and analysis.