Summary
You're building a vacation rental booking site. Listing search results must be relevant for all customers, and you need more resources to manually augment listings with keywords for properties that come and go.
You learned about using semantic search in Azure Database for PostgreSQL Flexible Server to query using embeddings generated by Azure OpenAI. You accomplished this search by:
- Enabling the
vectorandazure_aiextensions. - Creating vector columns to store embeddings.
- Generating and storing embeddings.
- Querying the database using a query vector.
Without semantic search, queries such as "sunny" don't match products called "bright natural light" even though it's an intended match. While this issue could be solved by adding extra product keywords, this process creates a problem when creating new property listings. The product could be undiscoverable without an exact keyword match if the keywords aren't added.
Listings that are hard to find are hard to book. Irrelevant search results degrade the user experience, and manual keyword maintenance increases human costs. On the other hand, Azure OpenAI automates synonym matching by generating embedding vectors, and Azure Database for PostgreSQL does the vector query heavy lifting. This combination provides a rich & relevant search experience without tedious keyword optimization.