Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela, 2020Advances in Neural Information Processing Systems (NeurIPS) 33DOI: 10.48550/arXiv.2005.11401 - Presents the original Retrieve-Augmented Generation (RAG) architecture, describing its components and how they work together to provide factual and contextually informed responses.
Question Answering with RAG, LangChain Team, 2024 (LangChain) - Official documentation for building RAG applications using the LangChain framework, including examples for setting up pipelines and running queries.
Building a RAG Application, LlamaIndex Team, 2024 (LlamaIndex) - Official documentation offering guidance and code examples for constructing Retrieve-Augmented Generation pipelines with the LlamaIndex framework.