XRAG and RAG-Performance
About XRAG
DocAILab/XRAG
XRAG: eXamining the Core - Benchmarking Foundational Component Modules in Advanced Retrieval-Augmented Generation
This project helps developers and researchers evaluate different components of Retrieval-Augmented Generation (RAG) systems. It takes various RAG configurations, such as different retrievers, embeddings, and Large Language Models, and outputs performance metrics and visualizations. The primary users are AI/ML engineers and researchers building or optimizing RAG applications.
About RAG-Performance
SciPhi-AI/RAG-Performance
Measuring RAG solutions throughput and latency
This tool helps RAG (Retrieval-Augmented Generation) solution developers compare the performance of different RAG frameworks when ingesting data. It takes common RAG frameworks and benchmark datasets (like Wikipedia articles or various text/PDF files) as input. It then measures and outputs key performance metrics such as data ingestion time, tokens processed per second, and megabytes processed per second, helping developers choose the most efficient framework for their specific application.
Related comparisons
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