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Advanced RAG Techniques is a comprehensive collection of tutorials and implementations focused on advanced Retrieval-Augmented Generation (RAG) systems. It is designed to help practitioners move beyond basic RAG setups and explore techniques that improve retrieval quality, context construction, and answer robustness. The repository organizes techniques into categories such as foundational RAG, query enhancement, context enrichment, and advanced retrieval, making it easier to navigate specific areas of interest. It includes hands-on Jupyter notebooks and runnable scripts that show how to implement ideas like optimizing chunk sizes, proposition chunking, HyDE/HyPE query transformations, fusion retrieval, reranking, and ensemble retrieval. There is also an evaluation section that demonstrates how to measure RAG performance and compare different configurations in a systematic way.
Features
- Curated library of advanced RAG techniques across multiple categories
- Hands-on Jupyter notebooks and runnable scripts for each technique
- Examples of query enhancement methods like HyDE and HyPE
- Context enrichment patterns such as semantic chunking and contextual compression
- Advanced retrieval strategies including fusion retrieval, reranking, and ensemble methods
- Evaluation utilities and guidelines for benchmarking different RAG configurations
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