Abstract

The assessment of chemical ingredients in cosmetic products against individual skin profiles is a largely manual and error-prone task. Existing consumer applications have primarily focused on the branding level and are without the depth of reasoning at the molecular level. Additionally, large language models (LLMs) without retrieval grounding generate hallucinations, resulting in unsafe recommendations and posing safety issues. This work presents a Chemical Compounds Recommendation Engine that integrates Optical Character Recognition (OCR) and a hierarchically structured Retrieval-Augmented Generation (RAG) framework to provide evidence-based recommendations on skincare ingredients. This engine processes a user query (optional product label image and skin type profile), retrieves relevant dermatology transcripts, employs evidence-based reasoning, and guides a structured response in JSON format (each recommendation is evidence-based and cited). Under a strict grounding condition, hallucinations are limited to a configurable evidence-overlap threshold (i.e., the rate = 2.7%). Role-Based Access Control (RBAC) provides a protective separation of doctor-level formulation safety analysis and consumer-level formulation safety analysis within a single microservices architecture (React, Spring Boot, Flask, and MongoDB) Provenance Tracking. Measuring 850 unique query responses, a separation F1 = 0.889 with 97.3% accurate grounding and 3.2 s latency is achieved, far surpassing the reliance on keyword, TF-IDF, ungrounded BERT, and zero retrieval GPT-4 benchmarks.

Authors

L. Suryaprasad, Vasudev Joshi, Eshwari, A. Haneen, M.S. Kendagannaswamy
JSS Science and Technology University, India

Keywords

Skincare AI, Chemical Recommendation Engine, Retrieval-Augmented Generation, Optical Character Recognition, Dermatology Informatics, Vector Databases, Role-Based Access Control, Microservices Architecture