用AI自动分析小角散射数据，支持自然语言交互。
SasAgent: Multi-Agent AI System for Small-Angle Scattering Data Analysis
- 分角色协作：协调员+三个专用代理处理数据计算与拟合。
- 能准确计算散射长度密度并生成符合实验的数据。
- 适合材料、物理研究者快速上手科学数据分析。
我们提出SasAgent，一个基于大语言模型（LLMs）的多智能体系统，通过调用SasView软件工具实现小角散射（SAS）数据的自动化分析，并支持用户以文本输入方式进行交互。SasAgent包含一个协调员智能体，负责理解用户指令并分配任务给三个专业代理：散射长度密度（SLD）计算、合成数据生成和实验数据拟合。这些代理利用源自SasView Python库的友好型工具高效执行任务，包括模型数据工具、检索增强生成（RAG）文档工具、凸峰拟合工具和SLD计算器工具。系统提供基于Gradio的友好界面，提升易用性。通过多种案例展示，SasAgent具备解析复杂指令、精确计算SLD、生成高保真散射数据及高精度拟合实验数据的能力。本工作展示了基于LLM的AI系统在简化科研流程、推动SAS研究自动化方面的潜力。
原文摘要 · Abstract (English)
We introduce SasAgent, a multi-agent AI system powered by large language models (LLMs) that automates small-angle scattering (SAS) data analysis by leveraging tools from the SasView software and enables user interaction via text input. SasAgent features a coordinator agent that interprets user prompts and delegates tasks to three specialized agents for scattering length density (SLD) calculation, synthetic data generation, and experimental data fitting. These agents utilize LLM-friendly tools to execute tasks efficiently. These tools, including the model data tool, Retrieval-Augmented Generation (RAG) documentation tool, bump fitting tool, and SLD calculator tool, are derived from the SasView Python library. A user-friendly Gradio-based interface enhances user accessibility. Through diverse examples, we demonstrate SasAgent's ability to interpret complex prompts, calculate SLDs, generate accurate scattering data, and fit experimental datasets with high precision. This work showcases the potential of LLM-driven AI systems to streamline scientific workflows and enhance automation in SAS research.
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