Development of an Offline RAG Chatbot for Answering Food Hygiene and Safety Questions Based on Vietnamese Legal Frameworks

This paper presents the design, development, and implementation of an offline chatbot system specialized in answering food safety -related questions, relying entirely on Vietnamese legal documents. The system employs Retrieval-Augmented Generation (RAG) to ensure accurate and contextually relevant responses without internet dependency, a critical feature for low -connectivity environments. Key highlights include robust Vietnamese language support, a flexible vector database using Chroma for seamless legal content updates, and the integration of Qwen2.5:7B-Instruct-Q4_0 as the local language model, selected after comparative testing against DeepSeek-R1, Gemma3:1B, and Mistral. Embeddings are generated using BAAI/bge -small-en-v1.5. By processing Vietna mese queries and retrieving from a localized knowledge base, the chatbot delivers reliable guidance to stakeholders such as food producers, traders, and consumers. Evaluations demonstrate high accuracy in Vietnamese Q&A, stable offline operation, and adapt ability to evolving regulations, with discussions on limitations and future enhancements.