AI Semantic Search for Your Website with Azure Cosmos DB | E-commerce

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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