RAG-Driven-Generative-AI

Why we included this project

Denis Rothman's Packt book on retrieval augmented generation comes with this companion repository, and the notebooks are the real value: they take RAG from theory to working code, chapter by chapter. Teams building a grounded question-answering system will find the full pipeline laid out in runnable form, from data preparation and embedding generation to vector stores built on Deep Lake and Pinecone, with LlamaIndex handling indexing and querying. The examples cover more than the basic happy path, including adaptive RAG with human feedback, scaling a Pinecone index to larger datasets, knowledge-graph retrieval over Wikipedia, and multimodal RAG that mixes text and images. Since the notebooks run in Colab and stay current with newer model APIs, they work as a living reference for comparing approaches, not just a static tutorial. Developers learning RAG and small teams prototyping retrieval pipelines will both find concrete patterns they can copy and adapt to their own data.