AI-Powered XtraReports in XAF: Unlocking DevExpress Enhancements

AI-Powered XtraReports in XAF: Unlocking DevExpress Enhancements

Today is Friday, so I decided to take it easy with my integration research. When I woke up, I decided that I just wanted to read the source code of DevExpress AI integrations to get inspired. I began by reading the official blog post about AI and reporting (DevExpress Blog Post). Then, as usual, I proceeded to fork the repository to make my own modifications.

After completing the typical cloning procedure in Visual Studio, I realized that to use the AI functionalities of XtraReport, you don’t need any special version of the report viewer.

The only requirement is to have the NuGet reference as shown below:


    <ItemGroup>
        <PackageReference Include="DevExpress.AIIntegration.Blazor.Reporting.Viewer" Version="24.2.1-alpha-24260" />
    </ItemGroup>
    

Then, add the report integration as shown below:


    config.AddBlazorReportingAIIntegration(config =>
    {
        config.SummarizeBehavior = SummarizeBehavior.Abstractive;
        config.AvailableLanguages = new List<LanguageItem>
        {
            new LanguageItem { Key = "de", Text = "German" },
            new LanguageItem { Key = "es", Text = "Spanish" },
            new LanguageItem { Key = "en", Text = "English" },
            new LanguageItem { Key = "ru", Text = "Russian" },
            new LanguageItem { Key = "it", Text = "Italian" }
        };
    });
    

After completing these steps, your report viewer will display a little star in the options menu, where you can invoke the AI operations.

You can find the source code for this example in my GitHub repository: https://github.com/egarim/XafSmartEditors

Till next time, XAF out!!!

The New Era of Smart Editors: Creating a RAG system using XAF and the new Blazor chat component

The New Era of Smart Editors: Creating a RAG system using XAF and the new Blazor chat component

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:

Implement a Property Editor Based on Custom Components (Blazor)

Implementing the Property Editor

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.

namespace XafSmartEditors.Razor.RagChat
{
    public interface IRagData
    {
        Stream FileContent { get; set; }
        string Prompt { get; set; }
        string FileName { get; set; }
    }
}

Non-Persistent Implementation

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:

namespace XafSmartEditors.Blazor.Server.Editors
{
    [PropertyEditor(typeof(IRagData), true)]
    public class IRagDataPropertyEditor : BlazorPropertyEditorBase, IComplexViewItem
    {
        private IObjectSpace _objectSpace;
        private XafApplication _application;

        public IRagDataPropertyEditor(Type objectType, IModelMemberViewItem model) : base(objectType, model) { }

        public void Setup(IObjectSpace objectSpace, XafApplication application)
        {
            _objectSpace = objectSpace;
            _application = application;
        }

        public override RagDataComponentModel ComponentModel => (RagDataComponentModel)base.ComponentModel;

        protected override IComponentModel CreateComponentModel()
        {
            var model = new RagDataComponentModel();

            model.ValueChanged = EventCallback.Factory.Create<IRagData>(this, value =>
            {
                model.Value = value;
                OnControlValueChanged();
                WriteValue();
            });

            return model;
        }

        protected override void ReadValueCore()
        {
            base.ReadValueCore();
            ComponentModel.Value = (IRagData)PropertyValue;
        }

        protected override object GetControlValueCore() => ComponentModel.Value;

        protected override void ApplyReadOnly()
        {
            base.ApplyReadOnly();
            ComponentModel?.SetAttribute("readonly", !AllowEdit);
        }
    }
}

Bringing It All Together

Now, let’s create a domain object that can feed the content of a file to our chat component:

namespace XafSmartEditors.Module.BusinessObjects
{
    [DefaultClassOptions]
    public class PdfFile : BaseObject
    {
        public PdfFile(Session session) : base(session) { }

        string prompt;
        string name;
        FileData file;

        public FileData File
        {
            get => file;
            set => SetPropertyValue(nameof(File), ref file, value);
        }

        public string Name
        {
            get => name;
            set => SetPropertyValue(nameof(Name), ref name, value);
        }

        public string Prompt
        {
            get => prompt;
            set => SetPropertyValue(nameof(Prompt), ref prompt, value);
        }
    }
}

Creating the Controller

We are almost done! Now, we need to create a controller with a popup action:

namespace XafSmartEditors.Module.Controllers
{
    public class OpenChatController : ViewController
    {
        Popup

WindowShowAction Chat;

        public OpenChatController()
        {
            this.TargetObjectType = typeof(PdfFile);
            Chat = new PopupWindowShowAction(this, "ChatAction", "View");
            Chat.Caption = "Chat";
            Chat.ImageName = "artificial_intelligence";
            Chat.Execute += Chat_Execute;
            Chat.CustomizePopupWindowParams += Chat_CustomizePopupWindowParams;
        }

        private void Chat_Execute(object sender, PopupWindowShowActionExecuteEventArgs e) { }

        private void Chat_CustomizePopupWindowParams(object sender, CustomizePopupWindowParamsEventArgs e)
        {
            PdfFile pdfFile = this.View.CurrentObject as PdfFile;
            var os = this.Application.CreateObjectSpace(typeof(ChatView));
            var chatView = os.CreateObject<ChatView>();

            MemoryStream memoryStream = new MemoryStream();
            pdfFile.File.SaveToStream(memoryStream);
            memoryStream.Seek(0, SeekOrigin.Begin);

            chatView.RagData = os.CreateObject<IRagDataImp>();
            chatView.RagData.FileName = pdfFile.File.FileName;
            chatView.RagData.Prompt = !string.IsNullOrEmpty(pdfFile.Prompt) ? pdfFile.Prompt : DefaultPrompt;
            chatView.RagData.FileContent = memoryStream;

            DetailView detailView = this.Application.CreateDetailView(os, chatView);
            detailView.Caption = $"Chat with Document | {pdfFile.File.FileName.Trim()}";

            e.View = detailView;
        }
    }
}

Conclusion

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.

Until next time, XAF out!

How ARM, x86, and Itanium Architectures Affect .NET Developers

How ARM, x86, and Itanium Architectures Affect .NET Developers

The ARM, x86, and Itanium CPU architectures each have unique characteristics that impact .NET developers. Understanding how these architectures affect your code, along with the importance of using appropriate NuGet packages, is crucial for developing efficient and compatible applications.

ARM Architecture and .NET Development

1. Performance and Optimization:

  • Energy Efficiency: ARM processors are known for their power efficiency, benefiting .NET applications on devices like mobile phones and tablets with longer battery life and reduced thermal output.
  • Performance: ARM processors may exhibit different performance characteristics compared to x86 processors. Developers need to optimize their code to ensure efficient execution on ARM architecture.

2. Cross-Platform Development:

  • .NET Core and .NET 5+: These versions support cross-platform development, allowing code to run on Windows, macOS, and Linux, including ARM-based versions.
  • Compatibility: Ensuring .NET applications are compatible with ARM devices may require testing and modifications to address architecture-specific issues.

3. Tooling and Development Environment:

  • Visual Studio and Visual Studio Code: Both provide support for ARM development, though there may be differences in features and performance compared to x86 environments.
  • Emulators and Physical Devices: Testing on actual ARM hardware or using emulators helps identify performance bottlenecks and compatibility issues.

x86 Architecture and .NET Development

1. Performance and Optimization:

  • Processing Power: x86 processors are known for high performance and are widely used in desktops, servers, and high-end gaming.
  • Instruction Set Complexity: The complex instruction set of x86 (CISC) allows for efficient execution of certain tasks, which can differ from ARM’s RISC approach.

2. Compatibility:

  • Legacy Applications: x86’s extensive history means many enterprise and legacy applications are optimized for this architecture.
  • NuGet Packages: Ensuring that NuGet packages target x86 or are architecture-agnostic is crucial for maintaining compatibility and performance.

3. Development Tools:

  • Comprehensive Support: x86 development benefits from mature tools and extensive resources available in Visual Studio and other IDEs.

Itanium Architecture and .NET Development

1. Performance and Optimization:

  • High-End Computing: Itanium processors were designed for high-end computing tasks, such as large-scale data processing and enterprise servers.
  • EPIC Architecture: Itanium uses Explicitly Parallel Instruction Computing (EPIC), which requires different optimization strategies compared to x86 and ARM.

2. Limited Support:

  • Niche Market: Itanium has a smaller market presence, primarily in enterprise environments.
  • .NET Support: .NET support for Itanium is limited, requiring careful consideration of architecture-specific issues.

CPU Architecture and Code Impact

1. Instruction Sets and Performance:

  • Differences: x86 (CISC), ARM (RISC), and Itanium (EPIC) have different instruction sets, affecting code efficiency. Optimizations effective on one architecture might not work well on another.
  • Compiler Optimizations: .NET compilers optimize code for specific architectures, but understanding the underlying architecture helps write more efficient code.

2. Multi-Platform Development:

    • Conditional Compilation: .NET supports conditional compilation for architecture-specific code optimizations.

    #if ARM
    // ARM-specific code
    #elif x86
    // x86-specific code
    #elif Itanium
    // Itanium-specific code
    #endif
    
  • Libraries and Dependencies: Ensure all libraries and dependencies in your .NET project are compatible with the target CPU architecture. Use NuGet packages that are either architecture-agnostic or specifically target your architecture.

3. Debugging and Testing:

  • Architecture-Specific Bugs: Bugs may manifest differently across ARM, x86, and Itanium. Rigorous testing on all target architectures is essential.
  • Performance Testing: Conduct performance testing on each architecture to identify and resolve any specific issues.

Supported CPU Architectures in .NET

1. .NET Core and .NET 5+:

  • x86 and x64: Full support for 32-bit and 64-bit x86 architectures across all major operating systems.
  • ARM32 and ARM64: Support for 32-bit and 64-bit ARM architectures, including Windows on ARM, Linux on ARM, and macOS on ARM (Apple Silicon).
  • Itanium: Limited support, mainly in specific enterprise scenarios.

2. .NET Framework:

  • x86 and x64: Primarily designed for Windows, the .NET Framework supports both 32-bit and 64-bit x86 architectures.
  • Limited ARM and Itanium Support: The traditional .NET Framework has limited support for ARM and Itanium, mainly for older devices and specific enterprise applications.

3. .NET MAUI and Xamarin:

  • Mobile Development: .NET MAUI (Multi-platform App UI) and Xamarin provide extensive support for ARM architectures, targeting Android and iOS devices which predominantly use ARM processors.

Using NuGet Packages

1. Architecture-Agnostic Packages:

  • Compatibility: Use NuGet packages that are agnostic to CPU architecture whenever possible. These packages are designed to work across different architectures without modification.
  • Example: Common libraries like Newtonsoft.Json, which work across ARM, x86, and Itanium.

2. Architecture-Specific Packages:

  • Performance: For performance-critical applications, use NuGet packages optimized for the target architecture.
  • Example: Graphics processing libraries optimized for x86 may need alternatives for ARM or Itanium.

Conclusion

For .NET developers, understanding the impact of ARM, x86, and Itanium architectures is essential for creating efficient, cross-platform applications. The differences in CPU architectures affect performance, compatibility, and optimization strategies. By leveraging cross-platform capabilities of .NET, using appropriate NuGet packages, and testing thoroughly on all target architectures, developers can ensure their applications run smoothly across ARM, x86, and Itanium devices.