You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: README.md
+3-4Lines changed: 3 additions & 4 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -17,18 +17,17 @@ products:
17
17
- azure-sqlserver-vm
18
18
- dotnet
19
19
- azure-openai
20
-
urlFragment: azure-sql-db-session-recommender
21
-
name: Session Recommender using Azure SQL DB, Open AI and Vector Search
20
+
name: Retriveal Augmented Generation with Azure SQL DB and OpenAI
22
21
description: Build a session recommender using Jamstack and Event-Driven architecture, using Azure SQL DB to store and search vectors embeddings generated using OpenAI
# Session Assistant Sample - Retriveal Augmented Generation with Azure SQL DB and OpenAI
27
24
28
25
This sample demonstrates how to build a session recommender using Jamstack and Event-Driven architecture, using Azure SQL DB to store and search vectors embeddings generated using OpenAI. The solution is built using Azure Static Web Apps, Azure Functions, Azure SQL Database, and Azure OpenAI.
29
26
30
27
A fully working, production ready, version of this sample, that has been used at [VS Live](https://vslive.com/) conferences, is available here: https://ai.lasvegas.vslive.com/
This repository is a evoution of the [Session Recommender](https://github.com/azure-samples/azure-sql-db-session-recommender) sample. In addition to vector search, also Retrival Augmented Generation (RAG) is used to generate the response to the user query. If you are completely new to this topic, you may want to start there, and then come back here.
0 commit comments