A full-stack tutorial that combines systematic theory with hands-on projects for building retrieval-augmented generation (RAG) intelligent question-answering and knowledge retrieval systems. It guides developers from basic concepts through advanced architectures such as Graph RAG and multimodal retrieval.
Project overview
Addresses the problem of scattered and unsystematic RAG learning materials by providing a structured curriculum that progresses from foundational concepts to production-oriented practices.
Project type
RAG
Use cases
Knowledge Q&A
Deployment
Refer to project documentation
License
License pending
Best for
Developers, AI engineers, and researchers seeking a structured, full-stack educational resource to understand and build production-ready RAG systems.
Key capabilities
Guides learners from basic concepts to advanced applications within a structured curriculum framework.
Teaches data loading from multiple formats and explains various text chunking strategies.
Includes practices for multimodal embedding and combined text plus image retrieval.
Limitations and risks
Some specific tutorial sections, such as the multimodal embedding practice and chapter 10, are currently documented as under optimization or planning.
Getting started
Prepare a Python 3.12.7 environment and Docker, and verify that any external models, APIs, or services required for RAG inference are available. Follow the hands-on chapter tutorials sequentially, which requires Linux command-line proficiency.