DeepSeek AI Integration with Blazor Smart TextArea

29 Jun 20269 minutes to read

The Blazor Smart TextArea component provides AI-powered autocompletion for context-aware text input, typically using OpenAI or Azure OpenAI. This guide explains how to integrate the DeepSeek AI service with the Smart TextArea using the IChatInferenceService interface, enabling custom AI-driven responses in a Blazor Web App.

Setting Up DeepSeek

  1. Obtain a DeepSeek API Key
    Create an account at DeepSeek Platform, sign in, and navigate to API Keys to generate an API key.
  2. Review Model Specifications
    Refer to DeepSeek Models Documentation for details on available models (e.g., deepseek-chat).

Create a DeepSeek AI Service

Create a service class to manage interactions with the DeepSeek API, including authentication and response processing for the Smart TextArea.

  1. Create a Services folder in your project.
  2. Add a new file named DeepSeekAIService.cs in the Services folder.
  3. Implement the service as shown below, storing the API key securely in a configuration file or environment variable (e.g., appsettings.json).
using System.Net;
using System.Text;
using System.Text.Json;
using Microsoft.Extensions.AI;

public class DeepSeekAIService
{
    private readonly string _apiKey;
    private readonly string _modelName = "deepseek-chat"; // Example model
    private readonly string _endpoint = "https://api.deepseek.com/v1/chat/completions";
    private static readonly HttpClient HttpClient = new(new SocketsHttpHandler
    {
        PooledConnectionLifetime = TimeSpan.FromMinutes(30),
        EnableMultipleHttp2Connections = true
    })
    {
        DefaultRequestVersion = HttpVersion.Version20 // Fallback to HTTP/2 for compatibility
    };
    private static readonly JsonSerializerOptions JsonOptions = new()
    {
        PropertyNamingPolicy = JsonNamingPolicy.CamelCase
    };

    public DeepSeekAIService(IConfiguration configuration)
    {
        _apiKey = configuration["DeepSeek:ApiKey"] ?? throw new ArgumentNullException("DeepSeek API key is missing.");
        if (!HttpClient.DefaultRequestHeaders.Contains("Authorization"))
        {
            HttpClient.DefaultRequestHeaders.Clear();
            HttpClient.DefaultRequestHeaders.Add("Authorization", $"Bearer {_apiKey}");
        }
    }

    public async Task<string> CompleteAsync(IList<ChatMessage> chatMessages)
    {
        var requestBody = new DeepSeekChatRequest
        {
            Model = _modelName,
            Temperature = 0.7f, // Controls response randomness (0.0 to 1.0)
            Messages = chatMessages.Select(m => new DeepSeekMessage
            {
                Role = m.Role == ChatRole.User ? "user" : "system", // Align with DeepSeek API roles
                Content = m.Text
            }).ToList()
        };

        var content = new StringContent(JsonSerializer.Serialize(requestBody, JsonOptions), Encoding.UTF8, "application/json");

        try
        {
            var response = await HttpClient.PostAsync(_endpoint, content);
            response.EnsureSuccessStatusCode();
            var responseString = await response.Content.ReadAsStringAsync();
            var responseObject = JsonSerializer.Deserialize<DeepSeekChatResponse>(responseString, JsonOptions);
            return responseObject?.Choices?.FirstOrDefault()?.Message?.Content ?? "No response from DeepSeek.";
        }
        catch (Exception ex) when (ex is HttpRequestException || ex is JsonException)
        {
            throw new InvalidOperationException("Failed to communicate with DeepSeek API.", ex);
        }
    }
}

NOTE

Store the DeepSeek API key in appsettings.json (e.g., { "DeepSeek": { "ApiKey": "your-api-key" } }) or as an environment variable to ensure security.

Define Request and Response Models

Define C# classes to match the DeepSeek API’s JSON request and response format.

  1. Create a new file named DeepSeekModels.cs in the Services folder.
  2. Add the following model classes:
public class DeepSeekMessage
{
    public string Role { get; set; }
    public string Content { get; set; }
}

public class DeepSeekChatRequest
{
    public string Model { get; set; }
    public float Temperature { get; set; }
    public List<DeepSeekMessage> Messages { get; set; }
}

public class DeepSeekChatResponse
{
    public List<DeepSeekChoice> Choices { get; set; }
}

public class DeepSeekChoice
{
    public DeepSeekMessage Message { get; set; }
}

Create a Custom AI Service

Implement the IChatInferenceService interface to connect the Smart TextArea to the DeepSeek service, acting as a bridge for AI-generated responses.

  1. Create a new file named DeepSeekInferenceService.cs in the Services folder.
  2. Add the following implementation:
using Syncfusion.Blazor.AI;
using System.Threading.Tasks;

public class DeepSeekInferenceService : IChatInferenceService
{
    private readonly DeepSeekAIService _deepSeekService;

    public DeepSeekInferenceService(DeepSeekAIService deepSeekService)
    {
        _deepSeekService = deepSeekService;
    }

    public async Task<string> GenerateResponseAsync(ChatParameters options)
    {
        return await _deepSeekService.CompleteAsync(options.Messages);
    }
}

Configure the Blazor App

Register the DeepSeek service and IChatInferenceService implementation in the dependency injection container.

Update the ~/Program.cs file as follows:

using Microsoft.AspNetCore.Components;
using Microsoft.AspNetCore.Components.Web;
using Syncfusion.Blazor;
using Syncfusion.Blazor.AI;

var builder = WebApplication.CreateBuilder(args);
....

builder.Services.AddSyncfusionBlazor();
builder.Services.AddSyncfusionSmartComponents();
builder.Services.AddSingleton<DeepSeekAIService>();
builder.Services.AddSingleton<IChatInferenceService, DeepSeekInferenceService>();

var app = builder.Build();
// ...

Use DeepSeek AI with Smart TextArea

Add the Smart TextArea component to a Razor file (e.g., ~/Pages/Home.razor) to use DeepSeek AI for autocompletion:

@using Syncfusion.Blazor.SmartComponents

<SfSmartTextArea UserRole="@userRole" UserPhrases="@userPhrases" Placeholder="Enter your queries here" @bind-Value="prompt" Width="75%" RowCount="5">
</SfSmartTextArea>

@code {
    private string? prompt;
    // Defines the context for AI autocompletion
    private string userRole = "Customer support representative";
    // Predefined phrases for AI to suggest during typing
    private string[] userPhrases = [
        "Thank you for reaching out.",
        "Please provide more details.",
        "We are investigating your issue."
    ];
}

Test the Integration

  1. Ensure all services are registered in Program.cs and the Smart TextArea is added to a Razor file.
  2. Run the application using Ctrl+F5 (Windows) or ⌘+F5 (macOS).
  3. Type phrases like “Thank” or “Please provide” in the Smart TextArea to verify that DeepSeek AI generates appropriate suggestions.
  4. Check that suggestions appear as configured (e.g., inline or pop-up, based on the ShowSuggestionOnPopup setting).

Smart TextArea with DeepSeek AI

Troubleshooting

If the DeepSeek AI integration does not work, try the following:

  • No Suggestions Displayed: Verify that the DeepSeek API key and model name are correct in the configuration. Check the DeepSeekAIService implementation for errors.
  • HTTP Request Failures: Ensure a stable internet connection and that the DeepSeek API endpoint (https://api.deepseek.com/v1/chat/completions) is accessible. Test with HTTP/2 if compatibility issues arise.
  • Service Registration Errors: Confirm that DeepSeekAIService and DeepSeekInferenceService are registered in Program.cs.

See Also