Ingesting data via LlamaHub
As we saw in Chapter 3, Kickstarting Your Journey with LlamaIndex, one of the first steps in a RAG workflow is to ingest and process our proprietary data. We already discovered the concepts of documents and nodes, which are used to organize the data and prepare it for indexing. I’ve also briefly introduced the LlamaHub data loaders as a way to easily ingest data into LlamaIndex. It’s time to examine these steps in more detail and gradually learn how to infuse LLM applications with our own, proprietary knowledge. Before we continue, though, I’d like to emphasize some very common challenges encountered at this step:
- No matter how effective our RAG pipeline is, at the end of the day, the quality of the final result will largely depend on the quality of the initial data. To overcome this challenge, make sure you start by cleaning up your data first. Eliminate potential duplicates and errors. While not exactly duplicates, redundant...
