Summary

In this chapter, we tackled the complexities of using RAG-driven generative AI, focusing on the essential role of document embeddings when handling large datasets. We saw how to go from raw texts to embeddings and store them in vector stores. Vector stores such as Activeloop, unlike parametric generative AI models, provide API tools and visual interfaces that allow us to see embedded text at any moment.

A RAG pipeline detailed the organizational process of integrating OpenAI embeddings into Activeloop Deep Lake vector stores. The RAG pipeline was broken down into distinct components that can vary from one project to another. This separation allows multiple teams to work simultaneously without dependency, accelerating development and facilitating specialized focus on individual aspects, such as data collection, embedding processing, and query generation for the augmented generation AI process.

We then built a three-component RAG pipeline, beginning by highlighting the...