Towards a RAG-based Summarization Agent for the Electron-Ion Collider
CoRR(2024)
摘要
The complexity and sheer volume of information encompassing documents,
papers, data, and other resources from large-scale experiments demand
significant time and effort to navigate, making the task of accessing and
utilizing these varied forms of information daunting, particularly for new
collaborators and early-career scientists. To tackle this issue, a Retrieval
Augmented Generation (RAG)–based Summarization AI for EIC (RAGS4EIC) is under
development. This AI-Agent not only condenses information but also effectively
references relevant responses, offering substantial advantages for
collaborators. Our project involves a two-step approach: first, querying a
comprehensive vector database containing all pertinent experiment information;
second, utilizing a Large Language Model (LLM) to generate concise summaries
enriched with citations based on user queries and retrieved data. We describe
the evaluation methods that use RAG assessments (RAGAs) scoring mechanisms to
assess the effectiveness of responses. Furthermore, we describe the concept of
prompt template-based instruction-tuning which provides flexibility and
accuracy in summarization. Importantly, the implementation relies on LangChain,
which serves as the foundation of our entire workflow. This integration ensures
efficiency and scalability, facilitating smooth deployment and accessibility
for various user groups within the Electron Ion Collider (EIC) community. This
innovative AI-driven framework not only simplifies the understanding of vast
datasets but also encourages collaborative participation, thereby empowering
researchers. As a demonstration, a web application has been developed to
explain each stage of the RAG Agent development in detail.
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