A RAG book repo that updates faster than its print edition

What it does This is the official companion repo for the Packt book RAG-Driven Generative AI. It delivers chapter-by-chapter Jupyter notebooks that walk through building retrieval-augmented generation pipelines with LlamaIndex, Deep Lake, Pinecone, and Chroma, using OpenAI and Hugging Face models for generation and embedding.

The interesting bit Most book repos fossilize after publication; this one keeps adding “bonus” notebooks that track new model releases—xAI’s Grok, OpenAI’s o1-preview and o3 reasoning models, and GPT-4.5-preview—turning a static syllabus into a living curriculum.

Key highlights

• Full RAG lifecycle coverage: chunking, embedding, indexing, retrieval, ranking, and augmented generation
• Multimodal pipeline example using drone imagery and text with modular RAG architecture
• Multiple vector store backends: Deep Lake, Pinecone, and Chroma
• Adaptive RAG chapter incorporating expert human feedback to refine retrieval
• Every notebook ships with one-click Colab and Kaggle launch badges
• Bonus notebooks for recent models including reasoning APIs and agentic previews

Caveats

• This is courseware, not a library: you get educational notebooks, not an installable package or CLI tool
• Most examples depend on third-party APIs (OpenAI, Pinecone, xAI) and won’t run fully offline
• The README is essentially a chapter index; deep architectural explanations live in the book itself

Verdict Worth bookmarking if you’re learning RAG by example or need starter templates combining LlamaIndex with commercial vector stores. Pass if you’re hunting for a production framework or a fully open-source, self-hosted stack.