This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
A method that uses instruction tuning to adapt LLMs for knowledge-intensive tasks. RankRAG simultaneously trains the models for context ranking and answer generation, enhancing their retrieval-augmented generation (RAG) capabilities.
NVIDIA and Georgia Tech researchers introduce RankRAG, a novel framework instruction-tuning a single LLM for top-k context ranking and answer generation. Aiming to improve RAG systems, it enhances context relevance assessment and answer generation.
This guide explains how to build and use knowledge graphs with R2R. It covers setup, basic example, construction, navigation, querying, visualization, and advanced examples.
Case study on measuring context relevance in retrieval-augmented generation systems using Ragas, TruLens, and DeepEval. Develop practical strategies to evaluate the accuracy and relevance of generated context.
