Multi-agent system
In this section, we see how we can create a system that considers different agents and a set of tools (such as ML models). The entire code can be found in the Multi_Model–Travel_Planning_System.py script.
As a general overview, the system implements a travel planning assistant that uses several agents to create personalized travel plans. The system then combines weather prediction, hotel recommendations, itinerary planning, and email summarization. In other words, we have four different agents, each dealing with a different aspect of travel planning:
WeatherAnalysisAgent: Uses a random forest regressor to predict the best time to visit a location based on historical weather data. Trains on past weather data (month, latitude, longitude, and weather score) and predicts the best months for travel based on weather scores. This agent then uses an ML model to conduct predictions (a model that is trained specifically for the system).HotelRecommenderAgent...
