.Net: added two agentic tutorials, local data base and ling file for RAG

[tgaidar]

Motivation and Context

Description

Contribution Checklist

• The code builds clean without any errors or warnings
• The PR follows the SK Contribution Guidelines and the pre-submission formatting script raises no violations
• All unit tests pass, and I have added new tests where possible
• I didn't break anyone 😄

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[dmytrostruk]

@tgaidar Thanks for your PR! I left initial comments, but before resolving them, let's sync internally.

[dmytrostruk]

Let's rename the file to 11-RAG-with-sliding-window.ipynb to keep file naming consistent.

[dmytrostruk]

It's better to keep output sections empty, so the notebook doesn't show the information from your local run. It's possible to clear all output sections in VS Code:

Same in 11-RAG file.

[dmytrostruk]

I'm not sure if it's necessary to perform test console output at the beginning of the file. We also don't have it in other notebooks.

Instead, it's always a good approach to start a notebook with a short description about its purpose.

Same in 11-RAG file.

[dmytrostruk]

Code section that follows this description uses new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential()), which means that only OpenAI API can be used in this example.

In other notebooks, we configure either OpenAI or Azure OpenAI based on user preference with the following logic:

var (useAzureOpenAI, model, azureEndpoint, apiKey, orgId) = Settings.LoadFromFile();

if (useAzureOpenAI)
    builder.AddAzureOpenAIChatCompletion(model, azureEndpoint, apiKey);
else
    builder.AddOpenAIChatCompletion(model, apiKey, orgId);

[dmytrostruk]

Suggested change

	"//get the endpoint from the environment variable with AI backend used by the kernel\n",
	"// get the endpoint from the environment variable with AI backend used by the kernel\n",

Try to leverage AI to improve code quality and comments wherever possible.

Also, even though the comment says AI backend used by the kernel, it looks like this example doesn't use Semantic Kernel at all. Only chat completion and image generation APIs are used. Since the file name says Agentic tutorial, does it make sense to use SK Agents in this example? The example contains some interaction with data, so maybe function/tool calling can be involved as well? These are just the ideas how to improve the tutorial.

[dmytrostruk]

What would be the source of DefaultAzureCredential when running it from the notebook? Is it environment variables?

[dmytrostruk]

It looks like this code is not required, since chatUpdates variable is not used in the example. The system prompt can be sent to AI together with user prompt in one request.

[dmytrostruk]

I can't find any functions declared in code sections above, should we modify the text here?

[dmytrostruk]

This section can be formatted as numeric list for better readability.

[dmytrostruk]

This block can be removed, since chatUpdates variable is not used, and system prompt can be sent together with user prompt in one request.