Wander Buddy ✈️

Wander buddy
Hybrid query - AQL
cuGraph - Shortest Flight
Wander Buddy in action
Hybrid query - API
Graph DB - NetworkX

Inspiration

WanderBuddy was inspired by the desire to simplify vacation planning and provide a smarter way for travelers to find the best flights, hotels, and activities. The challenge in the travel industry is fragmented data across multiple platforms. We aimed to create an AI-powered solution that integrates real-time data and graph-based technology to improve the experience.

What It Does

WanderBuddy is an AI-powered travel assistant that:

Provides personalized travel itineraries based on user preferences.
Uses natural language queries to search for flights, hotels, and attractions.
Leverages graph databases (ArangoDB) to store and retrieve travel data efficiently.
Visualizes travel routes and suggests optimal itineraries using AI and graph-based algorithms.

How We Built It

Backend: Built with Python, integrating LangChain for handling queries, and OpenAI’s GPT-4o for processing natural language.
Data Storage: We used ArangoDB for graph-based storage of travel data like cities, flights, hotels, and activities.
Graph-Based Search: Utilized cuGraph for fast graph algorithms like shortest-path search to suggest the best travel routes.
Visualization: Travel data is visualized using NetworkX and Matplotlib, providing dynamic graphs that represent flight routes and connections between cities.

Challenges We Ran Into

Hybrid Query Handling: Dynamically deciding between querying the graph database (ArangoDB) and external APIs (e.g., Booking.com) based on data availability was tricky.
API Integration: Integrating and managing multiple external APIs (for real-time data) required careful planning to ensure efficient data storage and retrieval.
Video Editing: Editing a professional-looking video to demonstrate our app’s features and keeping it within the time limit was challenging.

Accomplishments That We're Proud Of

Successfully integrating AI (GPT-4o) with graph databases (ArangoDB) to create a seamless travel planning experience.
Building a dynamic hybrid query system that combines the power of graph-based storage with real-time API data.
Delivering an engaging and informative demo video that effectively showcases the product.

What We Learned

Graph databases (ArangoDB) are incredibly powerful for managing complex relationships between entities (e.g., cities, flights, hotels).
Combining AI with graph-based systems enhances efficiency in delivering personalized insights to users.
The importance of dynamic data retrieval and how to handle hybrid systems efficiently for scalability.

What's Next for WanderBuddy

Integrate real-time flight price updates and multi-city travel support.
Improve visualization using advanced Matplotlib features.
Scale the system to handle millions of nodes for a smooth user experience as more users join the platform.