Retrieval-Augmented Language Models Enable Scalable Chemical Source Classification in Metabolomics Workflows

There is a growing need for scalable chemical classification to support the interpretation of exposomics and metabolomics data. While structural categorization has been largely automated, functional and exposure-based labeling of chemicals remains a manual and time-consuming process. Here, we present <i>chemsource</i>, a flexible framework that integrates large language models (LLMs) with retrieval-augmented generation (RAG) to automate chemical classification. <i>chemsource</i> retrieves descriptive text from Wikipedia or PubMed abstracts based on chemical names and prompts LLMs to assign user-defined categories based on the retrieved content. We demonstrate classification into five exposure categories: endogenous metabolites, food molecules, drugs, personal care products, industrial chemicals, and combinations of these possibilities. Benchmarking against manually curated labels for 4,953 compounds showed 75% overall agreement, with category-level recall exceeding 75% across all classes. Expert review indicated that most discrepancies could be attributed to prompt ambiguity and incomplete manual labels rather than model failure. To demonstrate the utility of <i>chemsource</i> in metabolomics workflow, we applied it to eight public untargeted metabolomics data sets, revealing distinct exposure patterns across human biospecimens, mouse tissues, environmental dust, and consumer product extracts. <i>chemsource</i> is customizable via prompt editing, enabling diverse classification tasks without requiring coding expertise. The tool is freely available as a Python package (https://pypi.org/project/chemsource/). Text retrieval is free; classification requires user-supplied LLM API access.