The project involves developing a travel planning assistant powered by a large language model (LLM) that suggests personalized itineraries and points of interest. The system integrates web search to gather real-time travel information and recommendations. Additionally, it employs Retrieval-Augmented Generation (RAG) over a vector database to enhance the LLM's responses with relevant, context-aware data. Users receive dynamic, accurate travel suggestions tailored to their preferences and current trends, improving their travel experience.
- Write prmpt for travel itinerary agents
- Setup tol for RAG search
- Use embedding system to publish website related info.
- Create tool to access Google maps information and reviews