using Azure.AI.OpenAI;
using DevExpress.AIIntegration;
using Microsoft.Extensions.AI;
using Microsoft.SemanticKernel.Connectors.InMemory;
using System;
using System.Windows.Forms;

internal static class Program {
    [STAThread]
    static void Main() {
        Application.EnableVisualStyles();
        Application.SetCompatibleTextRenderingDefault(false);

         var container = AIExtensionsContainerDesktop.Default;

        // The following code uses an Azure OpenAI embedding model.
        var embeddingGenerator = new AzureOpenAIClient(AzureOpenAIEndpoint, AzureOpenAIKey)
                        .GetEmbeddingClient("text-embedding-3-small").AsIEmbeddingGenerator();

        // Create an in-memory vector collection to store vectorized records.
        var vectorCollection = new InMemoryCollection<string, VectorStoreRecord>("Cars", new InMemoryCollectionOptions{ EmbeddingGenerator = embeddingGenerator});

        // Register the vector collection in the AI container.
        container.RegisterVectorCollection(vectorCollection);
        Application.Run(new Form1());
    }
}

Imports Azure.AI.OpenAI
Imports DevExpress.AIIntegration
Imports Microsoft.Extensions.AI
Imports Microsoft.SemanticKernel.Connectors.InMemory
Imports System
Imports System.Windows.Forms

Friend Module Program
    <STAThread>
    Sub Main()
        Application.EnableVisualStyles()
        Application.SetCompatibleTextRenderingDefault(False)

        Dim container = AIExtensionsContainerDesktop.Default

        ' The following code uses an Azure OpenAI embedding model.
        Dim embeddingGenerator = New AzureOpenAIClient(AzureOpenAIEndpoint, AzureOpenAIKey) _
                            .GetEmbeddingClient("text-embedding-3-small").AsIEmbeddingGenerator()

        ' Create an in-memory vector collection to store vectorized records.
        Dim vectorCollection As New InMemoryCollection(Of String, VectorStoreRecord)("Cars", New InMemoryCollectionOptions With {.EmbeddingGenerator = embeddingGenerator})

        ' Register the embedding generator in the AI container.
        container.AddEmbeddingGenerator(embeddingGenerator)

        ' Register the vector collection in the AI container.
        container.AddVectorCollection(vectorCollection)
        Application.Run(New Form1())
    End Sub
End Module

using DevExpress.AIIntegration;
using DevExpress.AIIntegration.SemanticSearch;
using DevExpress.AIIntegration.WinForms;
using Microsoft.Extensions.VectorData;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using System.Windows.Forms;

public partial class Form1 : DevExpress.XtraBars.Ribbon.RibbonForm {
    public Form1() {
        InitializeComponent();
        // Bind the Grid Control to data.
        gridControl1.DataSource = DataHelper.Items;
        // Enable search mode.
        gridView1.OptionsFind.Behavior = DevExpress.XtraEditors.FindPanelBehavior.Search;
        // Initialize the vector store.
        Task.Run(InitializeVectorStore);
    }

    async Task InitializeVectorStore() {
        // Get the default AI extensions container.
        var container = AIExtensionsContainerDesktop.Default;
        try {
            // Create a vector store collection named "Cars" with DataHelper.Items data.
            var collection = container.GetVectorCollection<string, VectorStoreRecord>("Cars");

            if (await collection.CollectionExistsAsync())
                return;

            var records = DataHelper.Items.Select((item, index) => new VectorStoreRecord{
                Key = item.Id.ToString(), // Use the item ID as the key.
                Vector = string.Join(". ", item.Trademark, item.Model, item.Description) // Store the generated vector.
            });

            await collection.EnsureCollectionExistsAsync();
            await collection.UpsertAsync(records);
        }
        catch (Exception ex) {
            // Display an error message if the operation fails.
            MessageBox.Show(ex.Message, "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
        }
    }
}

// Define a record stored in the vector store for semantic search.
public class VectorStoreRecord {
    [VectorStoreKey]
    // A unique identifier for the record in a vector store.
    public string Key { get; set; }

    // Azure OpenAI (embedding model) produces 1536-dimensional vectors.
    // Specify how vector data is stored and compared in a vector store.
    // Use cosine similarity as the distance function for comparisons.
    [VectorStoreVector(1536, DistanceFunction = DistanceFunction.CosineDistance)]
    public string Vector { get; set; }
}

public class DataItem {
    public Guid Id { get; set; }
    public string Trademark { get; set; }
    public string Model { get; set; }
    public string Description { get; set; }
}

Imports DevExpress.AIIntegration
Imports DevExpress.AIIntegration.SemanticSearch
Imports DevExpress.AIIntegration.WinForms
Imports Microsoft.Extensions.VectorData
Imports System
Imports System.Collections.Generic
Imports System.Linq
Imports System.Threading.Tasks
Imports System.Windows.Forms

Public Partial Class Form1
    Inherits DevExpress.XtraBars.Ribbon.RibbonForm

    Public Sub New()
        InitializeComponent()
        ' Bind the Grid Control to data.
        gridControl1.DataSource = DataHelper.Items
        ' Enable search mode.
        gridView1.OptionsFind.Behavior = DevExpress.XtraEditors.FindPanelBehavior.Search
        ' Initialize the vector store.
        Task.Run(AddressOf InitializeVectorStore)
    End Sub

    Private Async Function InitializeVectorStore() As Task
        ' Get the default AI extensions container.
        Dim container = AIExtensionsContainerDesktop.Default
        Try
            ' Create a vector store collection named "Cars" with DataHelper.Items data.
            Dim collection = container.GetVectorCollection(Of String, VectorStoreRecord)("Cars")

            If Await collection.CollectionExistsAsync() Then
                Return
            End If

            Dim records = DataHelper.Items.Select(Function(item, index) New VectorStoreRecord() With {
                .Key = item.Id.ToString(), ' Use the item ID as the key.
                .Vector = String.Join(". ", item.Trademark, item.Model, item.Description) ' Store the generated vector.
            })

            Await collection.EnsureCollectionExistsAsync()
            Await collection.UpsertAsync(records)
        Catch ex As Exception
            ' Display an error message if the operation fails.
            MessageBox.Show(ex.Message, "Error", MessageBoxButtons.OK, MessageBoxIcon.Error)
        End Try
    End Function
End Class

' Define a record stored in the vector store for semantic search.
Public Class VectorStoreRecord
    <VectorStoreKey>
    ' A unique identifier for the record in a vector store.
    Public Property Key As String

    ' Azure OpenAI (embedding model) produces 1536-dimensional vectors.
    ' Specify how vector data is stored and compared in a vector store.
    ' Use cosine similarity as the distance function for comparisons.
    <VectorStoreVector(1536, DistanceFunction:=DistanceFunction.CosineDistance)>
    Public Property Vector As String
End Class

Public Class DataItem
    Public Property Id As Guid
    Public Property Trademark As String
    Public Property Model As String
    Public Property Description As String
End Class

public Form1() {
    InitializeComponent();
    gridLookUpEdit1.Properties.DataSource = DataHelper.Items;
    gridLookUpEdit1.Properties.DisplayMember = "Model";
    gridLookUpEdit1.Properties.ValueMember = "Id";

    behaviorManager1.Attach<SemanticSearchBehavior>(gridLookUpEdit1.Properties.View, behavior => {
        behavior.Properties.VectorCollectionName = "Cars";
        behavior.Properties.DataSourceKeyField = "Id";
        behavior.Properties.SearchResultCount = 10;
        behavior.Properties.SearchMode = ControlSearchMode.Semantic;
        // It is implied, that Cosine Distance filtering is used for the vector store.
        // Lower = more similar
        behavior.Properties.ScoreThreshold = 0.5D;
        behavior.Properties.ScoreThresholdFilter = ScoreThresholdFilter.LessOrEqual;
    });
}

internal static class Program {
    [STAThread]
    static void Main() {
        //...
        // The Initialize() method forcibly initializes the behavior manager in .NET Framework apps.
        DevExpress.AIIntegration.WinForms.BehaviorInitializer.Initialize();
        Application.Run(new Form1());
    }
}

using Microsoft.Extensions.VectorData;

// Define a record stored in the vector store for semantic search.
public class VectorStoreRecord {
    // A unique identifier for the record in a vector store.
    [VectorStoreKey]
    public string Key { get; set; }

    // Azure OpenAI (embedding model) produces 1536-dimensional vectors.
    // Specify how vector data is stored and compared in a vector store.
    // This example uses cosine similarity as the distance function for comparisons.
    [VectorStoreVector(1536, DistanceFunction = DistanceFunction.CosineDistance)]
    public string Vector { get; set; }
}

// Higher = more similar
semanticSearchBehavior.ScoreThreshold = 0.5;
semanticSearchBehavior.ScoreThresholdFilter = ScoreThresholdFilter.GreaterOrEqual;

// Lower = more similar
semanticSearchBehavior.ScoreThreshold = 0.5;
semanticSearchBehavior.ScoreThresholdFilter = ScoreThresholdFilter.LessOrEqual;