How to Build a Travel AI MVP with RAG and Function Calls: A Founder’s Guide to Fast Prototyping

Building a Travel AI MVP with RAG and Function Calls

As a founder and software engineer, I am always looking for ways to use AI technologies to build impactful products. In the travel industry, combining Retrieval-Augmented Generation (RAG) with function calls can create powerful MVPs. This blog shares how I built a Travel AI MVP, explains RAG and function calls, and compares different frameworks. I’ll also explain why LangChain was the best choice for this project.

What is RAG?

Retrieval-Augmented Generation (RAG) combines retrieval-based systems with generative AI models. Instead of relying only on pre-trained knowledge, RAG retrieves data from external sources like databases or APIs. Then, it uses this data to improve its responses.

RAG is useful for travel AI because it:

• Ensures accuracy by using real-time, domain-specific data.
• Scales well with large datasets.
• Reduces costs by avoiding extensive fine-tuning.

For example, a travel AI using RAG can fetch real-time flight schedules, hotel availability, or local attractions. This creates a more dynamic user experience.

What Are Function Calls?

Function calls let AI interact with external systems like APIs. In a travel MVP, function calls can:

• Check flight availability and prices.
• Fetch weather forecasts for specific locations.
• Plan routes with mapping services.

With function calls, the AI goes beyond answering questions. It performs real tasks, providing users with actionable results.

Comparing Frameworks: AutoGen, LangChain, Rasa, and LlamaIndex

While building this MVP, I explored several frameworks. Here’s how they compare:

• AutoGen:
  • Works well for automated workflows and multi-step conversations.
  • Lacks flexibility for custom integrations and retrieval systems.
• LangChain:
  • Strong support for tool integration and chaining tasks.
  • Works well with RAG and function calls.
  • Offers great community support but has a steeper learning curve.
• Rasa:
  • Good for building conversational AI with rule-based logic.
  • Requires extra effort to add RAG or dynamic API calls.
• LlamaIndex:
  • Excellent for indexing and retrieving documents.
  • Limited support for handling complex function calls.

Why LangChain is the Best Choice

LangChain stood out for its ability to combine tools effectively. Here are the key reasons:

1. Tool Support: LangChain makes it easy to integrate retrieval systems, APIs, and generative AI.
2. Flexibility: It allowed me to test different APIs, data sources, and strategies.
3. Community Help: LangChain’s active community and documentation sped up the process.

Steps to Build the MVP

1. Set Up Data Retrieval:
   • Use a vector database to store travel-related data like destinations, flights, and hotels.
   • Connect APIs for real-time updates.
2. Add Function Calls:
   • Integrate APIs for weather, bookings, and maps.
3. Use LangChain for Orchestration:
   • Combine data retrieval, function calls, and generative AI to handle user queries seamlessly.
4. Test and Improve:
   • Run tests with real-world scenarios. Adjust based on feedback.

Lessons Learned

• Keep It Simple: Start with a small, clear scope to avoid getting overwhelmed.
• Iterate Often: Test early and refine based on what works.
• Use Community Resources: Open-source tools and forums can save time and effort.

Final Thoughts

With RAG and function calls, I built a Travel AI MVP that delivers accurate, real-time information. LangChain’s features made it the best framework for this project. For founders and developers in Travel AI, this approach is a fast, scalable way to build an innovative product.

If you’re interested in learning more about RAG or AI tools, check out Nicedays. This platform offers resources and insights into the latest AI technologies.

The journey of building this MVP showed me how important it is to pick the right tools. With LangChain, I’m confident this Travel AI can grow and meet travelers’ needs worldwide.