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.