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RoamMind — Travel AI Assistant

2024–2025 | Python, FastAPI, Azure OpenAI, Cosmos DB, Semantic Kernel | GitHub

⭐ 12 stars | 4 forks

RoamMind is an AI-powered travel assistant designed to help users plan trips, discover destinations, and get personalized recommendations for flights, hotels, restaurants, and excursions. It combines conversational AI with a modular backend architecture to provide scalable, context-aware travel planning support.

Problem / Motivation

Planning a trip involves coordinating multiple elements like flights, accommodation, and activities. Users often face:

  • Difficulty managing multi-domain travel information in one place.
  • Overwhelming options with little personalized guidance.
  • Fragmented services requiring multiple apps or websites.
  • Limited AI assistants capable of handling complex, multi-step travel planning tasks.

RoamMind addresses these challenges by providing a single, intelligent conversational interface that integrates multiple travel domains and offers personalized, context-aware recommendations.

Core Functionalities

Conversational Interface

  • Engages users in natural language conversations for seamless travel planning.
  • Understands multi-turn dialogues, maintaining context throughout the interaction.

Multi-Domain Support

  • Provides recommendations and information for:
  • Flights
  • Hotels
  • Restaurants
  • Excursions and local activities

Travel Query Processing

  • Processes user input to extract relevant parameters like dates, locations, and preferences.
  • Uses dedicated skills for each travel domain to fetch accurate and structured information.

Conversation & State Management

  • Maintains conversation state using a central orchestrator, enabling multi-step travel planning.
  • Supports contextual follow-ups and personalized recommendations.

Data Enhancement

  • Enhances search results with LLM-powered responses via Azure OpenAI.
  • Provides detailed information, summaries, and suggestions to improve decision-making.

Robust Architecture

  • Modular design with separate layers for API, business logic, and infrastructure.
  • Dependency injection ensures loose coupling between orchestrator, skills, and infrastructure components.
  • Custom exception handling and validation for seamless user experience.

Technical Features

  • FastAPI: High-performance API for handling requests and integrating with front-end or other services.
  • Azure OpenAI: Advanced language processing for semantic understanding and response generation.
  • Cosmos DB Integration: Repository and data storage for travel-related information.
  • Modular Codebase: Structured into directories including api, core, infrastructure, models, skills, and utils.
  • Logging & Validation: Built-in utilities for debugging, monitoring, and error handling.

Challenges & Issues Addressed

  • Managing multi-domain travel information in a single conversational interface.
  • Maintaining context across multi-turn conversations.
  • Ensuring modularity and scalability for adding new travel domains or features.
  • Providing accurate and contextually relevant recommendations while handling exceptions gracefully.
  • Integrating AI models efficiently for real-time travel query responses.

Potential Applications

  • Personal Travel Planning: Assist users in booking flights, hotels, and activities.
  • Travel Agencies: Enhance customer support with AI-driven personalized recommendations.
  • Tourism Platforms: Provide interactive travel guidance to users.
  • AI Research: Serve as a testbed for conversational AI in multi-domain environments.

Future Enhancements

  • Integrate real-time flight and hotel availability APIs.
  • Expand excursion and activity recommendation capabilities with geolocation-based suggestions.
  • Add multi-language support for global users.
  • Implement more sophisticated RAG pipelines for real-time knowledge updates.
  • Improve conversational memory to handle extended multi-step itineraries.

Learning Outcomes

  • Developed a multi-domain conversational AI assistant integrating LLMs with structured travel data.
  • Built scalable, modular backend architecture with dependency injection and orchestrator design.
  • Gained expertise in Azure OpenAI integration for advanced semantic understanding.
  • Implemented conversational state management for multi-turn dialogues.
  • Learned practical techniques for combining LLM outputs with structured database queries.

Note

RoamMind is a fictional project created solely as a template and does not represent a real product. It serves as the core logic for building AI agent instructions and is not fully production-ready. Contributions are welcome if you want to enhance it. This template provides a foundational framework for a Travel AI Assistant, which you can customize to suit your requirements.