May 1, 2024
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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