A curated tutorial collection of 42+ runnable Jupyter notebooks covering foundational to advanced Retrieval-Augmented Generation techniques. Developers and researchers can clone the repository to learn and implement specific RAG patterns using accompanying code and documentation.
Project overview
Provides an extensive breadth of RAG techniques, spanning from foundational concepts to cutting-edge implementations, consolidated into a single practical learning resource.
Project type
RAG · Prompt Engineering · Learning Resources
Use cases
Knowledge Q&A · Search & Research · Learning & Education
Deployment
Refer to project documentation
License
License pending
Best for
Developers and researchers seeking a curated, runnable tutorial library to learn and implement diverse Retrieval-Augmented Generation techniques.
Key capabilities
A collection of 42+ Jupyter notebooks providing tutorials on various RAG techniques from foundational to advanced, including code and references.
Standalone Python scripts accompanying some of the notebooks for executing RAG techniques.
Tutorials covering query transformations, HyDE, and HyPE to improve retrieval effectiveness.
Tutorials demonstrating semantic chunking, proposition chunking, and contextual chunk headers to improve retrieval accuracy.
Limitations and risks
The project is licensed under a custom non-commercial license, restricting its use in commercial applications.
Getting started
Getting started requires cloning the repository, navigating to the technique of interest, and following the detailed implementation guide located in each technique's directory.
Alternatives and comparisons
Provides a collection of notebooks and recipes showcasing effective ways of using Claude, including Retrieval Augmented Generation tutorials and copy-able code snippets for easy integration.
Compiles large documents into a structured agent skill with on-demand chapter loading to reduce token usage and prevent hallucinations.
Provides a comprehensive, theory-and-practice guide to understand and build AI Native Agent systems, from core principles to multi-agent applications.
GitHub project description: This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
README: A community-driven hub of **42+ runnable notebooks** covering RAG techniques from foundational to cutting-edge - the intuition, the code, and the references to build more accurate…
README: This repository showcases a curated collection of advanced techniques designed to supercharge your RAG systems, enabling them to deliver more accurate, contextually relevant, and…
README: Our goal is to provide a valuable resource for researchers and practitioners looking to push the boundaries of what's possible with RAG.
README: - 🧠 State-of-the-art RAG enhancements - 📚 Comprehensive documentation for each technique - 🛠️ Practical implementation guidelines - 🌟 Regular updates with the latest advancements