AutoRAG: An Automated Tool for Optimizing Retrieval-Augmented Generation Pipelines

AutoRAG is a tool designed to optimize Retrieval-Augmented Generation (RAG) pipelines by evaluating various RAG modules with self-evaluation data to identify the best configuration for specific use cases. It automates data creation, performs optimization experiments, and supports deployment using a single YAML file. AutoRAG structures the pipeline into interconnected nodes and uses synthetic data from large language models (LLMs) for effective evaluation. Currently in its alpha phase, it shows promising potential for future development.