How to enhance your chatbot so it can retrieve data from multiple data sources & orchestrate its own plan with C# Semantic Kernel, planner & Azure OpenAI – part 2 (demo app implementation)

Developer Support

In this multi-part series, Jordan Bean shares how to enhance a chatbot to retrieve data from multiple data sources and orchestrate plans with C# Semantic Kernel, planner, and Azure Open AI.

In the previous post, we discussed how the RAG pattern isn’t enough to answer complex user questions. We talked about using Semantic Kernel to orchestrate AI calls can allow AI to generate its own plan for answering questions from various data sources.

In my GitHub repo, I have a sample application that demonstrates this idea.

Azure Architecture

In this example, all the APIs & web apps are hosted in Azure Container Apps with an Azure OpenAI service backend.

Use case

As a reminder, we are building a customer support chatbot for our fictional outdoor sporting equipment company. We want to be able to answer common customer support questions by utilizing our internal data sources.


Continue reading this post, as well as the full series on Jordan’s dev blog.


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