RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
RAG Foundry is a library designed to improve LLMs ability to use external information by fine-tuning models on specially created RAG-augmented datasets. The library helps create the data for training, given a RAG technique, helps easily train models using parameter-efficient finetuning (PEFT), and finally can help users measure the improved performance using various, RAG-specific metrics. The library is modular, workflows are customizable using configuration files.