Vector Database-Backed RAG for Enterprise HR Analytics

Abstract

This study addresses the inefficiencies of the manual faculty promotion evaluation process at Mindanao State University–Maigo College of Education, Science and Technology (MSU-MCEST), which follows the 2005 Revised Integrated Scheme for Ranking and Promotion (ISRP). The traditional paper-based approach is time-consuming, prone to human error, and requires extensive administrative effort. To address these challenges, the study developed an automated decision-support system using a Vector Database–Backed Retrieval-Augmented Generation (RAG) framework. The system integrates Optical Character Recognition (OCR), Natural Language Processing (NLP), and semantic vector embeddings to transform unstructured 201 files into structured evaluation reports. A service-oriented architecture was implemented using XAMPP for the web interface and Python FastAPI for machine learning services, with ChromaDB enabling efficient similarity search and retrieval. Evaluation using 100 faculty records (700 document pages) achieved a classification accuracy of 97.14% (F1 = 0.966) and reduced processing time from three days to four hours. Statistical analysis showed no significant difference between automated and manual scoring (p > 0.05). ISO 25010 evaluation results indicated high system acceptability (Mean = 3.653). The findings demonstrate that the proposed system improves efficiency, accuracy, and transparency in faculty promotion pre-evaluation while maintaining compliance with institutional policies.