Public service chatbots play a crucial role in providing efficient and accessible services to citizens, but their performance often falls short in specialized domains such as public administration. This paper presents an approach to enhance the performance of Vietnamese public service chatbots by integrating Retrieval-Augmented Generation (RAG). By leveraging Langchain’s flexible architecture, the chatbot can seamlessly make API calls and interact with databases, ensuring access to the most relevant and current information. Especially, we explored RAG with large language models like Vistral-7B-chat, combined with retrieval mechanisms, to provide contextually appropriate responses. Our experimental results demonstrate a significant improvement in response accuracy, addressing the limitations of generic open-domain models. This enhancement offers substantial benefits for public administrative interactions, significantly improving efficiency and quality in governmental applications.