RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Discover the many ways you can build RAG applications on top of Azure Database for PostgreSQL Flexible Servers. We'll start by using the pgvector extension for vector similarity search. Then we'll explore the new azure_ai extension which provides built-in functions for embeddings, summarization, sentiment analysis, and more. Finally, we'll demo the new azure_local_ai extension to efficiently run pre-trained models on our database server, which can be a great fit for RAG applications that require custom embedding models.

Presented by Joshua Johnson, Principal TPM on Azure PostgreSQL, and Pamela Fox, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **