In January, I volunteered at that New York City AI Tour Event and got to attend a few sessions in the afternoon.

I spent the morning at the OpenAI booth answering general questions about Azure OpenAI. It was fun to hear what sort of projects people have and how they are using AI in general.

I’d forgotten how sore you legs can get from just standing on a cement floor all day … and was reminded to wear sneakers to my next conference - NOT my normal leather boots.

Late last year I was looking for an AZD template that uses Chainlit for a UI application and deploys to Azure Container Apps, but couldn’t find one … so now I’m working on building one.

Chainlit OpenAI AssistantsAPI Azure Container Apps Starter

This is a starter template that works with Azure Developer CLI (AZD) to create a Chainlit application and deploy it to Azure in a matter of minutes. The application code currently uses OpenAI and OpenAI Assistants API - I’m working on adding more functionality to it soon.

This week I presented at the TechBash conference in Pocono Manor, PA. All the resources for the conference are available on the TechBash GitHub repo. Below are links to my presentations specifically.

It was great to see companies sending their employees to conferences again. This was my first time at TechBash, and I finally got to meet Alvin Ashcraft in person (the Morning Dew). I also met a bunch of people from the Philly area and around Pennsylvania, but there were attendees from all over-even a couple of guys from Panama!

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.