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

Microsoft Mechanics Podcast

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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