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

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


 


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