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Danielskry/Awesome-RAG
😎 Awesome list of Retrieval-Augmented Generation (RAG) applications in Generative AI.
An awesome-list of links covering the RAG ecosystem: architecture patterns, chunking and retrieval techniques, frameworks, vector databases, and evaluation tools. It's a reference page for someone starting out with RAG, not a library or implementation you install.
Organized into real categories (chunking, retrieval, databases, production concerns) with a table of contents, so it's navigable rather than one giant unordered list. Links mostly point to primary sources — arXiv papers, official framework docs, the actual GitHub repos — instead of secondary blog recaps. Actively maintained (pushed within the last week, 1.4k stars) and splits large sections like the Python ecosystem into separate docs instead of bloating the main README.
There's no code here, nothing to run — the entire value depends on external links staying alive, and awesome-lists rot fast once maintainers lose interest. No visible inclusion bar: production frameworks like Haystack sit next to random Medium posts and a single contributor's side project, with no indication which were actually tried versus just found via search. Several entries aren't RAG-specific at all (generic prompting guides, async Python docs) padding out sections that should be tighter. As a reference, it's fine for a first pass, but it won't tell you which of the twenty frameworks listed is actually worth your time — that judgment call is left entirely to the reader.