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 aka.ms/PGAIMechanics
Get started with Azure Database for PostgreSQL - Flexible Server at aka.ms/postgresql
To stay current with all the updates, check out our blog at aka.ms/azurepostgresblog
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#postgresql #azureai #vectorsearch #AzureDatabase

[@JoeBurnett]

This is an amazing capability! I can’t wait to start working with this on some of my own projects.

[@jay-dj4ui]

I love the vector columns saved with a relational database, usually, I thought there would be 2 DBs one is traditional DB and vector DB, but now you make them in the same spreadsheet. I am not sure about the performance.