SpeLL: An Agent for Natural Language-Driven Intelligent Spectral Modeling

Spectrum large language model (SpeLL) was developed to tackle core challenges in near-infrared (NIR) spectral data modelingthe high level of expertise and substantial workload required by researchers for method selection, implementation, and optimization, particularly in light of the growing number of spectral analysis techniques and specific application scenarios. SpeLL harnesses the transformative capabilities of large language models (LLMs) and retrieval-augmented generation (RAG) technology, integrating them through natural language interaction to transform complex spectral data modeling workflows into automated operations. The core strength of SpeLL lies in its dual RAG pathways. The Code RAG provides specialized code knowledge for spectral data analysis, enabling the LLM to generate robust and domain-specific analytical scripts that address the implementation and optimization of algorithms. The Data RAG stores historical spectral data sets rich in patterns and features, intelligently matching similar data to guide the choice of algorithms and optimize modeling tasks. By offering an end-to-end automated workflow encompassing natural language understanding, RAG-enhanced code generation and execution, and an Auto-Debug mechanism, SpeLL achieves intelligent and automated NIR spectral data modeling and analysis.光谱大语言模型（Spectrum Large Language Model，简称SpeLL）旨在解决近红外（NIR）光谱数据建模领域的核心挑战：当前研究人员在方法选择、算法实现与模型优化环节需具备极高专业素养，且需投入大量工作量，尤其随着光谱分析技术与特定应用场景的数量持续增长，该问题愈发凸显。SpeLL依托大语言模型（Large Language Models，LLMs）与检索增强生成（Retrieval-Augmented Generation，RAG）技术的颠覆性能力，通过自然语言交互将二者融合，将复杂的光谱数据建模工作流转化为自动化操作。SpeLL的核心优势在于其双路径RAG架构：代码RAG可为光谱数据分析提供专业代码知识，使大语言模型能够生成稳健且贴合领域需求的分析脚本，从而解决算法实现与优化问题；数据RAG则存储了蕴含丰富模式与特征的历史光谱数据集，可智能匹配相似数据，以指导算法选择并优化建模任务。通过提供涵盖自然语言理解、RAG增强的代码生成与执行，以及自动调试（Auto-Debug）机制的端到端自动化工作流，SpeLL实现了近红外光谱数据建模与分析的智能化与自动化。