Build low-latency recommendation engines with Azure Cosmos DB and Azure OpenAI Service. Elevate user experience with vector-based semantic search, going beyond traditional keyword limitations to deliver personalized recommendations in real-time. With pre-trained models stored in Azure Cosmos DB, tailor product predictions based on user interactions and preferences. Explore the power of augmented vector search for optimized results prioritized by relevance.

Kirill Gavrylyuk, Azure Cosmos DB General Manager, shows how to build recommendation systems with limitless scalability, leveraging pre-computed vectors and collaborative filtering for next-level, real-time insights.

► QUICK LINKS: 00:00 - Build a low latency recommendation engine 00:59 - Keyword search 01:46 - Vector-based semantic search 02:39 - Vector search built-in to Cosmos DB 03:56 - Model training 05:18 - Code for product predictions 06:02 - Test code for product prediction 06:39 - Augmented vector search 08:23 - Test code for augmented vector search 09:16 - Wrap up

► Link References

Walk through an example at https://aka.ms/CosmosDBvectorSample

Try out Cosmos DB for MongoDB for free at https://aka.ms/TryC4M

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