10 min
AI Semantic Search for Your Website with Azure Cosmos DB | E-commerce Microsoft Mechanics Podcast
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- Tech News
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
► Unfamiliar with Microsoft Mechanics?
As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.
• Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries
• Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog
• Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast
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• Follow us on Twitter: https://twitter.com/MSFTMechanics
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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
► Unfamiliar with Microsoft Mechanics?
As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.
• Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries
• Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog
• Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast
► Keep getting this insider knowledge, join us on social:
• Follow us on Twitter: https://twitter.com/MSFTMechanics
• Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/
• Enjoy us on Instagram: https://www.instagram.com/msftmechanics/
• Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics
10 min