AutoRAG and RAG_course
One tool provides an automated framework for the evaluation and optimization of RAG systems, while the other offers a collection of advanced techniques to implement within RAG systems, making them complementary resources.
About AutoRAG
Marker-Inc-Korea/AutoRAG
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
Provides end-to-end RAG pipeline optimization through YAML-driven configuration, encompassing document parsing, semantic chunking, and QA dataset generation with support for multiple parsing/chunking strategies simultaneously. Uses grid-search and metric-driven evaluation across retriever-generator combinations to identify optimal module configurations, with results tracked in a dashboard for deployment-ready pipeline export. Integrates with LlamaIndex, LangChain, and local embedding models, supporting both cloud APIs (OpenAI) and GPU-accelerated inference for custom models.
About RAG_course
justinzm/RAG_course
此存储库展示了用于检索增强生成（RAG）系统的各种先进技术。RAG 系统将信息检索与生成模型相结合，以提供准确且上下文丰富的响应。
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work