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This paper presents a retrieval-augmented question answering (RAG) system tailored for the Electron-Ion Collider (EIC) experiment, utilizing a local database of arXiv articles and an open-source LLaMA model. The system offers a cost-effective and privacy-preserving alternative to cloud-based solutions for answering domain-specific queries in experimental nuclear physics. The locally deployed RAG system avoids sending pre-publication scientific data to public domains.
A fully local RAG system slashes costs and ensures data privacy for answering complex physics questions, rivaling cloud-based solutions.
To harness the power of Language Models in answering domain specific specialized technical questions, Retrieval Augmented Generation (RAG) is been used widely. In this work, we have developed a Q\&A application inspired by the Retrieval Augmented Generation (RAG), which is comprised of an in-house database indexed on the arXiv articles related to the Electron-Ion Collider (EIC) experiment - one of the largest international scientific collaboration and incorporated an open-source LLaMA model for answer generation. This is an extension to it's proceeding application built on proprietary model and Cloud-hosted external knowledge-base for the EIC experiment. This locally-deployed RAG-system offers a cost-effective, resource-constraint alternative solution to build a RAG-assisted Q\&A application on answering domain-specific queries in the field of experimental nuclear physics. This set-up facilitates data-privacy, avoids sending any pre-publication scientific data and information to public domain. Future improvement will expand the knowledge base to encompass heterogeneous EIC-related publications and reports and upgrade the application pipeline orchestration to the LangGraph framework.
