AutoRAG

AutoRAG is an open-source tool that automatically finds the best Retrieval-Augmented Generation (RAG) pipeline for your specific data and use case. Designed to simplify the process of RAG evaluation, AutoRAG enables you to efficiently test and optimize various RAG module combinations using just a few lines of Python code. Traditionally, selecting and evaluating RAG pipelines is time-consuming and complex, but AutoRAG streamlines this process by automating the search for the optimal solution.

With AutoRAG, you can provide your own evaluation data to test different RAG strategies, helping you identify the most effective pipeline tailored to your needs. Whether for QA systems or other machine learning applications, AutoRAG ensures you deploy the most suitable RAG model without unnecessary trial and error.

Key Features:

• Automatic RAG Pipeline Optimization: Evaluates and identifies the best RAG pipeline for your specific dataset and use case.

• Ease of Use: Requires minimal setup and only a few lines of Python code to get started.

• Open-Source: Fully accessible on GitHub, promoting collaborative use and community-driven improvements.

• Custom Evaluation: Supports custom evaluation data, ensuring pipelines are fine-tuned to your exact requirements.

• Flexible RAG Module Testing: Easily evaluates various RAG module combinations to find the most optimal configuration.

AutoRAG eliminates the guesswork from RAG pipeline selection, empowering developers and researchers to optimize their models efficiently and deploy high-performance solutions tailored to their specific needs.