Mental notes on architecture, learning by reading source, and what’s next.
OK — so it’s time for a new article. Lately, I’ve been diving deep into the Oqtane framework, and it’s been a beautiful journey. It reminds me of my early days with XAF from Developer Express—when I learned to think in software architecture and modern design patterns by simply reading the code.Back then, documentation was scarce. The advice was: “Look at the code.” I did—and that shaped a big part of my software education. It taught me that good source code is often self-explanatory.
Even though XAF is still our main tool at the office (Xari & BIT Frameworks), we’re expanding. We’re researching new divisions for Flutter and React, since some projects already use those fronts with an XAF backend. I also wanted to explore building client-server apps with a single .NET codebase that includes mobile—another reason Oqtane caught my eye.
Why Oqtane Caught My Attention
The Oqtane team is very responsive on GitHub. You can open a discussion and get thoughtful replies quickly. The source code is clean and educational—perfect for learning by reading. There are plenty of talks and videos on architecture and module development; some are a bit dated, but if you cross-check with the code, you’ll be fine.
I’ve learned there are two steps to mastering a framework: (1) immerse yourself in material (videos, code, docs), and (2) explain it to someone else. These notes do both—part research, part knowledge sharing.
There’s one clip I couldn’t locate where Shaun Walker explains that .NET already provides the pieces for modern, multi-platform, server-and-client applications—but the ecosystem is fragmented. Oqtane unifies those pieces into a single .NET codebase. If I find it, I’ll make a highlight and share it.
On Learning and Time
I’m trying to publish as much as I can now because I’m about to start a new chapter: I’ll be joining the University of St. Petersburg to learn Russian as my second language. It’s a tough language—very different from Spanish or Italian—so I’ll likely have less time to write for a while. Better to document these experiments now than let them sit in my notes for months.
The New Era of Smart Editors: Developer Express and AI Integration
The new era of smart editors is already here. Developer Express has introduced AI functionality in many of their controls for .NET (Windows Forms, Blazor, WPF, MAUI).
This advancement will eventually come to XAF, but in the meantime, here at XARI, we are experimenting with XAF integrations to add value to our customers.
In this article, we are going to integrate the new chat component into an XAF application, and our first use case will be RAG (Retrieval-Augmented Generation). RAG is a system that combines external data sources with AI-generated responses, improving accuracy and relevance in answers by retrieving information from a document set or knowledge base and using it in conjunction with AI predictions.
To achieve this integration, we will follow the steps outlined in this tutorial:
When I implement my own property editor, I usually avoid doing so for primitive types because, in most cases, my property editor will need more information than a simple primitive value. For this implementation, I want to handle a custom value in my property editor. I typically create an interface to represent the type, ensuring compatibility with both XPO and EF Core.
After defining the type for my editor, I need to create a non-persistent implementation:
namespace XafSmartEditors.Razor.RagChat
{
[DomainComponent]
public class IRagDataImp : IRagData, IXafEntityObject, INotifyPropertyChanged
{
private void OnPropertyChanged([CallerMemberName] string propertyName = null)
{
PropertyChanged?.Invoke(this, new PropertyChangedEventArgs(propertyName));
}
public IRagDataImp()
{
Oid = Guid.NewGuid();
}
[DevExpress.ExpressApp.Data.Key]
[Browsable(false)]
public Guid Oid { get; set; }
private string prompt;
private string fileName;
private Stream fileContent;
public Stream FileContent
{
get => fileContent;
set
{
if (fileContent == value) return;
fileContent = value;
OnPropertyChanged();
}
}
public string FileName
{
get => fileName;
set
{
if (fileName == value) return;
fileName = value;
OnPropertyChanged();
}
}
public string Prompt
{
get => prompt;
set
{
if (prompt == value) return;
prompt = value;
OnPropertyChanged();
}
}
// IXafEntityObject members
void IXafEntityObject.OnCreated() { }
void IXafEntityObject.OnLoaded() { }
void IXafEntityObject.OnSaving() { }
public event PropertyChangedEventHandler PropertyChanged;
}
}
Creating the Blazor Chat Component
Now, it’s time to create our Blazor component and add the new DevExpress chat component for Blazor:
<DxAIChat CssClass="my-chat" Initialized="Initialized"
RenderMode="AnswerRenderMode.Markdown"
UseStreaming="true"
SizeMode="SizeMode.Medium">
<EmptyMessageAreaTemplate>
<div class="my-chat-ui-description">
<span style="font-weight: bold; color: #008000;">Rag Chat</span> Assistant is ready to answer your questions.
</div>
</EmptyMessageAreaTemplate>
<MessageContentTemplate>
<div class="my-chat-content">
@ToHtml(context.Content)
</div>
</MessageContentTemplate>
</DxAIChat>
@code {
IRagData _value;
[Parameter]
public IRagData Value
{
get => _value;
set => _value = value;
}
async Task Initialized(IAIChat chat)
{
await chat.UseAssistantAsync(new OpenAIAssistantOptions(
this.Value.FileName,
this.Value.FileContent,
this.Value.Prompt
));
}
MarkupString ToHtml(string text)
{
return (MarkupString)Markdown.ToHtml(text);
}
}
The main takeaway from this component is that it receives a parameter named Value of type IRagData, and we use this value to initialize the IAIChat service in the Initialized method.
Creating the Component Model
With the interface and domain component in place, we can now create the component model to communicate the value of our domain object with the Blazor component:
namespace XafSmartEditors.Razor.RagChat
{
public class RagDataComponentModel : ComponentModelBase
{
public IRagData Value
{
get => GetPropertyValue<IRagData>();
set => SetPropertyValue(value);
}
public EventCallback<IRagData> ValueChanged
{
get => GetPropertyValue<EventCallback<IRagData>>();
set => SetPropertyValue(value);
}
public override Type ComponentType => typeof(RagChat);
}
}
Creating the Property Editor
Finally, let’s create the property editor class that serves as a bridge between XAF and the new component:
That’s everything we need to create a RAG system using XAF and the new DevExpress Chat component. You can find the complete source code here: GitHub Repository.
If you want to meet and discuss AI, XAF, and .NET, feel free to schedule a meeting: Schedule a Meeting.