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How Azure AI Search powers RAG in ChatGPT and global scale apps

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Manage episode 448945259 series 1320201
Content provided by Jeremy Chapman and Microsoft Mechanics. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jeremy Chapman and Microsoft Mechanics or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

Millions of people use Azure AI Search every day without knowing it. You can enable your apps with the same search that enables retrieval-augmented generation (RAG) capabilities when you build Custom GPTs or attach files in your ChatGPT prompts.

Pablo Castro, Microsoft CVP and Distinguished Engineer Azure AI Search, joins Jeremy Chapman to share how with Azure AI Search, you can create custom applications that retrieve the most relevant information quickly and accurately, even from billions of records.

Manage massive-scale datasets while maintaining high-quality search results with ultra-compact, binary quantized vector search indexes that use Matryoshka Representation Learning (MRL) and oversampling to equal the search accuracy of vector indexes up to 96 times larger. These approaches drive significant cost savings by optimizing your vector indexes without compromising quality.

► QUICK LINKS: 00:00 - RAG powered by Azure AI Search 00:50 - Azure AI Search role in ChatGPT 02:01 - Azure AI Search use case - AT&T 03:27 - Start in Azure Portal 04:35 - Massive scale and vector index 06:08 - Scalar & Binary Quantization 07:21 - Martyoshka technique 09:07 - Oversampling 11:31 - How to build an app using Azure AI Search 13:00 - See it in action 14:28 - Enable binary quantization with oversampling 14:54 - Wrap up

► Link References

Get sample code on GitHub at https://aka.ms/SearchQuantizationSample

Check out search solutions at https://aka.ms/AzureAISearch

► 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/

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• Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

  continue reading

252 episoade

Artwork
iconDistribuie
 
Manage episode 448945259 series 1320201
Content provided by Jeremy Chapman and Microsoft Mechanics. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jeremy Chapman and Microsoft Mechanics or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

Millions of people use Azure AI Search every day without knowing it. You can enable your apps with the same search that enables retrieval-augmented generation (RAG) capabilities when you build Custom GPTs or attach files in your ChatGPT prompts.

Pablo Castro, Microsoft CVP and Distinguished Engineer Azure AI Search, joins Jeremy Chapman to share how with Azure AI Search, you can create custom applications that retrieve the most relevant information quickly and accurately, even from billions of records.

Manage massive-scale datasets while maintaining high-quality search results with ultra-compact, binary quantized vector search indexes that use Matryoshka Representation Learning (MRL) and oversampling to equal the search accuracy of vector indexes up to 96 times larger. These approaches drive significant cost savings by optimizing your vector indexes without compromising quality.

► QUICK LINKS: 00:00 - RAG powered by Azure AI Search 00:50 - Azure AI Search role in ChatGPT 02:01 - Azure AI Search use case - AT&T 03:27 - Start in Azure Portal 04:35 - Massive scale and vector index 06:08 - Scalar & Binary Quantization 07:21 - Martyoshka technique 09:07 - Oversampling 11:31 - How to build an app using Azure AI Search 13:00 - See it in action 14:28 - Enable binary quantization with oversampling 14:54 - Wrap up

► Link References

Get sample code on GitHub at https://aka.ms/SearchQuantizationSample

Check out search solutions at https://aka.ms/AzureAISearch

► 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

  continue reading

252 episoade

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