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, Vol. 33 (Curran Associates, Inc.)DOI: 10.48550/arXiv.2005.11401 - This paper introduced the concept of Retrieval-Augmented Generation (RAG), a method for combining a retriever with a generator for better knowledge-grounded responses.
Hugging Face Transformers Documentation, Hugging Face, 2024 (Hugging Face) - Provides official guides and API references for using the transformers library to load and run pre-trained models, including local LLMs.
OpenAI API Documentation, OpenAI, 2024 (OpenAI) - The official resource for integrating with OpenAI's models, detailing API usage, authentication, and specific model endpoints.
LangChain Documentation, LangChain, 2024 - Introduces the LangChain framework for building applications with LLMs, offering abstractions for connecting LLMs as generators in RAG pipelines.