Multimodal RAG with Docling

Retrieval Augmented Generation (RAG) is an architectural pattern that can be used to augment the performance of language models by recalling factual information from a knowledge base, and adding that information to the model query.

In this lab we will combine the skills we learned in the two previous labs to build a Docling-enhanced RAG system.

Prerequisites

This lab is a Jupyter notebook. Please follow the instructions in pre-work to run the lab.

Lab

To run the notebook from your command line in Jupyter using the active virtual environment from the pre-work, run:

jupyter notebook notebooks/RAG.ipynb

The path of the notebook file above is relative to the docling-workshop folder from the git clone in the pre-work.