{"id":26778622,"url":"https://github.com/shauryashaurya/brag","last_synced_at":"2026-04-11T14:35:59.933Z","repository":{"id":251247500,"uuid":"836722915","full_name":"shauryashaurya/bRAG","owner":"shauryashaurya","description":"Braggosaurus Rex, the fragrant one, dragon of prague, keeper of the ragtag 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\n\u003cbr/\u003e\n\u003cbr/\u003e\nB\u003cstrong\u003e\u003cfont background='#ffeb3b' color='red'\u003eRAG\u003c/font\u003e\u003c/strong\u003egosaurus Rex, the f\u003cstrong\u003e\u003cfont  background='#ffeb3b' color='red'\u003eRAG\u003c/font\u003e\u003c/strong\u003erant one, d\u003cstrong\u003e\u003cfont  background='#ffeb3b' color='red'\u003eRAG\u003c/font\u003e\u003c/strong\u003eon of p\u003cstrong\u003e\u003cfont  background='#ffeb3b' color='red'\u003eRAG\u003c/font\u003e\u003c/strong\u003eue, keeper of the \u003cstrong\u003e\u003cfont  background='#ffeb3b' color='red'\u003eRAG\u003c/font\u003e\u003c/strong\u003etag d\u003cstrong\u003e\u003cfont  background='#ffeb3b' color='red'\u003eRAG\u003c/font\u003e\u003c/strong\u003eoons...\n\n\u003cbr/\u003e \u003cbr/\u003e  \n\t\u003cimg src=\"./images/bRAG-cover-small.png\" width=\"95%\" align=\"center\" alt=\"Braggosaurus Rex, the fRAGrant one, dRAGon of pRAGue, keeper of the RAGtag dRAGoons... © Shaurya Agarwal, 2024\" /\u003e            \n\u003c/html\u003e              \n           \n          \nExploring [Retrieval-augmented generation](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) in all it's technical and mathematical glory.           \n            \n---          \n          \n# References          \n          \n* The OG paper that introduced **RAG**s:             \n\t* [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)            \n\t* also [pdf](https://arxiv.org/pdf/2005.11401)            \n* [DSPy](https://dspy.ai/), also on [GitHub](https://github.com/stanfordnlp/dspy)          \n          \n          \n* [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://arxiv.org/abs/2310.11511)          \n\t* [pdf](https://arxiv.org/pdf/2310.11511)          \n\t* [huggingface](https://huggingface.co/papers/2310.11511)          \n* [FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research](https://github.com/RUC-NLPIR/FlashRAG)          \n\t* [Paper](https://arxiv.org/abs/2405.13576), [PDF](https://arxiv.org/pdf/2405.13576)          \n* [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)          \n\t* [PDF](https://arxiv.org/pdf/2005.11401)          \n* [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906)          \n\t* [PDF](https://arxiv.org/pdf/2004.04906)          \n* maybe?           \n\t* [RAGFlow](https://github.com/infiniflow/ragflow)          \n\t* [DAIR.AI Prompt Engineering Guide](https://www.promptingguide.ai/), also [GitHub](https://github.com/dair-ai/Prompt-Engineering-Guide)          \n\t* [langchain-ai rag-from-scratch](https://github.com/langchain-ai/rag-from-scratch)    \n\t* [Recursive Language Models](https://arxiv.org/abs/2512.24601), [PDF](https://arxiv.org/pdf/2512.24601)\n\t\t* [The Google Cloud Community Article](https://discuss.google.dev/t/recursive-language-models-in-adk/323523)\n\t          \n\t             \n---","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshauryashaurya%2Fbrag","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshauryashaurya%2Fbrag","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshauryashaurya%2Fbrag/lists"}