A Comprehensive Study on Travel Guide Chatbot

Abstract- This paper introduces an AI-powered travel guide chatbot that utilizes a Retrieval- Augmented Generation (RAG) architecture to provide users with intelligent, context-sensitive assistance based on their own uploaded documents. The system is designed to handle various file formats containing travel-related content such as itineraries, brochures, and destination notes. By allowing users to interact with the chatbot through natural language queries, the platform delivers accurate and relevant responses grounded in the content of these documents. The core language model used in the system is Mistral AI, which delivers high-quality, efficient responses and supports multi-turn conversations. This approach ensures scalability and responsiveness while maintaining a high level of accuracy in information retrieval. The chatbot's versatility makes it suitable for a wide range of use cases, including travel planning, destination research, and itinerary refinement. The proposed system demonstrates the effectiveness of combining RAG architecture with privacy- focused design in the development of next-generation AI chat applications. A key innovation of the system lies in its privacy- first approach. Uploaded files are securely processed, ensuring that user data remains confidential at all times. The integration of Google authentication further enhances user experience by enabling session continuity and persistent access to chat history. This commitment to privacy makes the chatbot especially suitable for users handling sensitive or proprietary travel documents. Keywords: AI chatbot, travel guide, Retrieval- Augmented Generation, natural language processing, user privacy, Mistral AI, document- based QA.