A Retrieval-Augmented Natural Language Interface for Data Description and Meta-Analysis in the Pathogens-in-Foods (PIF) Database

Food-safety occurrence databases are increasingly important for surveillance, evidence appraisal, and quantitative risk assessment, yet their routine analytical use remains constrained by the need for database literacy and statistical programming. Building on the curated and harmonized Pathogens-in-Foods (PIF) database, we developed and evaluated a retrieval-augmented natural-language interface designed to support grounded querying and reproducible evidence synthesis. The system includes two complementary modes: an Open Chat Mode for exploratory, tool-mediated interrogation of the database and a Guided Meta-Analysis Mode that couples structured user input to a deterministic R-based analytical pipeline. Evaluation included four compact language models: Phi-4 Mini (3.8B), DeepSeek-R1 Tool-Calling (14B), Cogito (14B), and Qwen 3 (8B), together with Gemini 2.5 Pro as a larger proprietary baseline model. Within a 10-query benchmark, all models achieved 100% tool selection accuracy and retrieval correctness; for the five argument bearing queries, all models also achieved 100% argument extraction F1-score, indicating reliable grounding of database operations for the evaluated query set. In a guided case study on Toxoplasma in meat and meat products (153 records from 65 studies), the system achieved 100% numerical concordance and high visual informativeness; the highest report quality index was 93% with Qwen 3 (8B). Performance differences across models arose primarily from the factual precision and economy of their written interpretations rather than from failures in tool execution. These findings support hybrid, evidence-grounded analytical interfaces built on curated data resources and deterministic statistical backends as practical tools for accelerating surveillance-oriented evidence synthesis in food protection.