This study develops and evaluates two Retrieval-Augmented Generation (RAG)-based chatbot systems for municipal information services, offering deployment guidelines for municipalities with different technical capacities. The first system, built with LangChain, allows advanced code-level customization and flexibility for complex configurations and integration with existing infrastructure. The second system, constructed with Dify, is a GUI-based platform that supports rapid deployment and low-code operation. Both systems were equipped with key security features, including detection of personally identifiable information (PII), prevention of prompt injection, and request rate limiting. Evaluation was carried out using input data modeled after real administrative inquiries in simulated scenarios. Findings suggest that LangChain is more effective where strict oversight and detailed management are required, while Dify, despite limited code-level flexibility, excels in speed and ease of maintenance. This makes Dify a practical option for municipalities with limited technical expertise.
