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:
NOTE: In order to get the most out of this blog post, you should first read the related two posts.
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.
The code for this entry is in my Github repo semantic-kernel-getting-started under the notebooks folder:
This past month I’ve been focusing on Graph RAG. This entry is an attempt to capture some lessons learned and a place to itemize all the resources I’ve found useful.
NOTE: My approach to this topic was to find a way to improve a typical RAG implementation that only uses vector similarity searching.
Jason is an experienced developer and solutions architect focused on helping companies provide real business value with custom software development using the Microsoft stack of technologies. This includes C#, .NET, Azure and AI services. In 2024, Jason was awarded a Microsoft MVP in AI - highlighting his latest focus on AI and specifically Generative AI technologies in real world business applications. The prior seven years, he was a Microsoft Azure MVP.
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