Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI

Indexes increase precision and speed performances, but they offer more than that. Indexes transform retrieval-augmented generative AI by adding a layer of transparency. With an index, the source of a response generated by a RAG model is fully traceable, offering visibility into the precise location and detailed content of the data used. This improvement not only mitigates issues like bias and hallucinations but also addresses concerns around copyright and data integrity.

In this chapter, we’ll explore how indexed data allows for greater control over generative AI applications. If the output is unsatisfactory, it’s no longer a mystery why, since the index allows us to identify and examine the exact data source of the issue. This capability makes it possible to refine data inputs, tweak system configurations, or switch components, such as vector store software and generative models, to achieve better outcomes...