Retrieval-Augmented Generation Method in the Development of Large Language Model Chatbots for the Anambas Civil Registry Public Information Service

Abstract

Population administration services in the Anambas Islands Regency face significant challenges related to limited information access caused by geographical conditions. Service information on the official Disdukcapil website, which is passive, often makes it difficult for the public to obtain fast and relevant answers. This condition leads to service queue buildups and potentially decreases public satisfaction. As a solution, an intelligent chatbot application based on a Large Language Model (LLM) with a Retrieval-Augmented Generation (RAG) approach was developed. This method effectively combines precise information retrieval capabilities from a document database with the LLM's natural language understanding ability to produce contextual answers. The system's development process was carried out using the LangChain framework, Chroma vector store, and was integrated into a web interface as the frontend. Official Disdukcapil service information was processed through chunking, embedding, and RAG pipeline creation stages. The results showed that the chatbot was able to respond to inquiries about population services accurately and efficiently. Based on evaluations using the BERTScore metric, the system obtained average scores of 98,5% for Precision, 99,1% for Recall, and 98,8% for F1-Score. This system can be accessed from anywhere, greatly assisting people in remote areas, and serves as a potential initial prototype to support the digitization of public services in archipelago regions.