AutoRAG and RAG-FiT
AutoRAG provides systematic evaluation and optimization of RAG pipelines, while RAG-FiT enhances the language model component itself through fine-tuning—making them complementary tools that address different layers of RAG system improvement.
About AutoRAG
Marker-Inc-Korea/AutoRAG
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
This tool helps AI developers and researchers find the best Retrieval-Augmented Generation (RAG) pipeline for their specific data and use case. You provide your documents and an evaluation dataset (questions and their correct answers), and AutoRAG automatically tests various RAG components and configurations. The output is an optimized RAG pipeline that performs best for your application.
About RAG-FiT
IntelLabs/RAG-FiT
Framework for enhancing LLMs for RAG tasks using fine-tuning.
This framework helps AI developers improve how well Large Language Models (LLMs) answer questions using external knowledge. It takes your existing RAG (Retrieval Augmented Generation) technique and a dataset, then generates specialized data for fine-tuning your LLM. The output is a more accurate LLM and detailed metrics showing its improved performance in RAG tasks.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work