In case you missed this week’s .NET Conf Focus on AI you can find all the videos on the dotnet YouTube channel. The one I want to point out here is: Better Together: .NET Aspire and Semantic Kernel with Steve Sanderson and Matthew Bolanos.

Steve’s portion of the talk is a distilled version of his NDC talk earlier this year: How to add genuinely useful AI to your webapp (not just chatbots) where he introduced the eShopSupport project. If you are a .NET developer looking for ways to use AI in real world applications - you should absolutely watch that video.

Last week I wrote about the notebook I created when I was working out the flow of the property graph RAG implementation. In this entry I will go through the two projects I created to provide some reusable code as well as allow for better experimentation:

Related posts:

• Study Notes: Graph RAG - Property Graph RAG
• Study Notes: Graph RAG - Property Graph RAG (The Notebook)

NOTE: In order to get the most out of this blog post, you should first read the related two posts.

Last Thursday night I spoke at the Wisconsin .NET User Group near Milwaukee, WI. I was nice to meet so many .NET developers interested in RAG and AI.

To carry on the tradition from my Memphis talk, I gave the presentation a local look using images related to Milwaukee generated from Bing/create.

Talk: Getting Started with Retrieval Augmented Generation (RAG)

The presentation pdf can be downloaded here.

One of my demos failed to deploy before the talk, but I don’t think it was missed due to having so many good questions. It was really nice meeting a user group with so many core members. Plus the pizza was really good!

Monday I posted my notes on this last month’s study topic of property graph RAG, which has the general information I’ve collected. In this entry I want to go through some code I created in a polyglot notebook (ie. a notebook that has C# code instead of python), when I was working through the steps needed for a property graph RAG application.

Related posts:

• Study Notes: Graph RAG - Property Graph RAG
• Study Notes: Graph RAG - Property Graph RAG (The Projects)

Where To Get The Code

The code for this entry is in my Github repo semantic-kernel-getting-started under the notebooks folder:

• Study Notes: Graph RAG - Property Graph RAG (The Notebook)
• Study Notes: Graph RAG - Property Graph RAG (The Projects)

NOTE: My approach to this topic was to find a way to improve a typical RAG implementation that only uses vector similarity searching.

Yesterday I posted my notes from this week’s study topic of Text-to-SQL, which if you haven’t read it - provides more information and resources about the topic. In this entry I want to walk through a code sample I put together after playing with a few samples this week.

Where To Get The Code

The code for this entry is in my GitHub repo semantic-kernel-getting-started under the samples/demos/Text-to-Sql directory.

Originally I considered making this a review of the NL2SQL code sample, but I ended up needing to make some changes to it, so I just copied over some of their code for my sample - that is why the nl2sql.library project is there (also there is a Nl2Sql folder in the TextToSqlConsole project with some other files from their repo). If you are looking into Text-to-SQL, I highly recommend taking a look at their sample.

This week I’ve been researching Text-to-SQL (also known as Natural Language to SQL), below are my study notes to compile all the resources I’ve found on the topic to date. There is also a corresponding blog entry that walks through a code example.

NOTE: I am approaching this topic specifically looking at how it can be used to extend usage scenarios in a RAG application.

Background

Text-to-SQL (or Natural Language to SQL) is a pattern where the objective is to have an LLM generate SQL statements for a database using natural language.

Last night was the Season of AI presentation. We started with Bill Wilder presenting the fundamentals of Generative AI and quick introduction to Azure AI Studio, then I finished up with a .NET code walkthrough implement Retrieval Augmented Generation (RAG) using Semantic Kernel.

It was nice to see a lot of regular faces and meet several new people.

Demo Code

The demo code is on my GitHub repo BostonAzure-June2024 under a subdirectory.

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.

UPDATE: The Bing Search APIS are being deprecated. See the announcement for more details: Bing Search APIs retiring on August 11, 2025. I have updated the post and corresponding Github code to use the Brave connector instead.

A couple of weeks ago I thought I’d written my last of these blogs, mainly due to me getting more in depth with Semantic Kernel. However, after I watched Will Velida’s video Using Bing Search API in the Semantic Kernel SDK … I couldn’t help but wonder what the API calls were behind the scenes. Will does a great job at explaining how to use the plugin and the Bing resource needed to make calls to the search API, so I won’t get into that part of it - I want to focus on the usefulness and API calls made by the plugin.