PostgreSQL with Local Small Language Model and In-Database Vectorization | Azure

Improve search capabilities for your PostgreSQL-backed
applications using vector search and embeddings generated in
under 10 milliseconds without sending data outside your
PostgreSQL instance. Integrate real-time translation, sentiment
analysis, and advanced AI functionalities securely within your
database environment with Azure Local AI and Azure AI Service.
Combine the Azure Local AI extension with the Azure AI extension
to maximize the potential of AI-driven features in your
applications, such as semantic search and real-time data
translation, all while maintaining data security and efficiency.


Joshua Johnson, Principal Technical PM for Azure Database for
PostgreSQL, demonstrates how you can reduce latency and ensure
predictable performance by running locally deployed models,
making it ideal for highly transactional applications.


 


QUICK LINKS: 
00:00 - Improve search for PostgreSQL
01:21 - Increased speed
02:47 - Plain text descriptive query
03:20 - Improve search results
04:57 - Semantic search with vector embeddings
06:10 - Test it out
06:41 - Azure local AI extension with Azure AI Service
07:39 - Wrap up


 


Link References


Check out our previous episode on Azure AI extension at
https://aka.ms/PGAIMechanics 


Get started with Azure Database for PostgreSQL - Flexible Server
at https://aka.ms/postgresql 


To stay current with all the updates, check out our blog at
https://aka.ms/azurepostgresblog


 


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