Building an AI Travel Planner with Vector, Full-Text, and Hybrid Search in Azure Cosmos DB

This episode of Code with Azure Cosmos DB builds on the travel planning application started in episode one, showing how hybrid search combines vector similarity with full-text precision and how a single database can power both retrieval and booking workflows. The presenters are Marko Hotti, Senior Product Marketing Manager for Azure Cosmos DB, and Patty Chow, Product Manager at Azure Cosmos DB.

■ Search capabilities
- Vector search, full-text search, and hybrid search, each serving a different purpose
- Semantic reranking within hybrid search, demonstrated with a Barcelona hotel search
- How exact name matches and semantic results are handled together

■ Data architecture
- Keeping operational data and vector embeddings in one database instead of separate systems
- One SDK, one security model, and one database to operate
- The distinction between retrieval queries and transactional reads and writes

■ Booking workflow
- Moving from property search to date selection, availability checks, and reservations
- How inventory, bookings, and preferences are handled under the same system

■ What comes next
- Preview of the following episode: specialized agents and shared memory using the Agent Memory Toolkit

Developer teams building production AI applications that must retrieve live operational data and complete user tasks will find this useful. Viewers can expect to understand when to apply each search type, why keeping vectors and app data together affects retrieval accuracy, and how search and transactional operations fit into one system.