This is the fifth C# demo in The RAG Demo Chronicles (Blog Series) and has the most extensive use of Semantic Kernel out of all the demos I’ve reviewed.

The use of Retrieval Augmented Generation (RAG) is different with this project than the other demos I’ve reviewed - mainly because RAG is just one of its features.

With this demo, I also took the time to configure the optional authentication so I could play with the MS Graph plugin … and WOW! Just adding the web searcher and MS Graph plugin really make this a powerful demo app. It gives me a lot of ideas for my own projects.

This is the fourth C# demo in The RAG Demo Chronicles (Blog Series) and is the first demo so far that saves its history to a database.

This Retrieval Augmented Generation (RAG) demo is a little different than the last three because it primarily uses data from a database as the content to search instead of documents. It also uses Semantic Kernel more than other demos have, which is neat to see too.

This is the last in the family of Azure Search OpenAI demos that I’m covering (I’m not looking at the Java version). I reviewed the C# version and the Javascript/Typescript version earlier this month. Of the three I’m covering, this one seems to be the most active, popular and have the most documentation.

At the beginning of this month, the Hack Together: The AI Chat App Hack used this demo at the sample repository, marking it as a solid reference implementation for RAG.

This is the second in the family of Azure Search OpenAI demos that I’m reviewing. Last week I reviewed the C# version. As you’ll see below, the Javascript version is a bit different.

The user interface (UI) functionality is provided by a set of web components that you can add to about any web application (ie. React, Angular, Vue, etc.) - in fact the web application in the demo is written in React. Also the chat communication is written to match the HTTP protocol for AI chat app which means the frontend can communicate with any backend that matches that protocol.

1. azure-search-openai-demo-csharp - written in C#.
2. azure-search-openai-demo - written in python.
3. azure-search-openai-javascript - written in javascript/typescript.
4. azure-search-openai-demo-java - written in java.

This post is about #1 above, I will cover #2 and #3 in later posts, but I will leave the Java version to someone else to review.

If you are like me, a full stack C# developer who is attempting to get up to speed on how GenAI technologies are going to show up in our business applictions - then after you get the first demo up and running, this demo (also by Michael Washington) is a great next step.

RAG (Retrieval Augmented Generation) applications typically have the following steps:

In my last blog entry I mentioned how I’ve been spending a lot of time learning AI related topics lately. Recently the newest research topic is RAG (Retrieval Augmented Generation). The more I learn about RAG, the more I am convinced it is how my clients will be able to take advantage of GenAI in the short term.

For those of you who are not familiar with the topic, you may have heard a presentation or blog focusing on “How to Chat with Your Documents” or something along those lines - these are usually about RAG. The idea is an application that is like ChatGPT but only looks at your data and documents.