Getting Started with Smart Components using Gemini AI Service

25 Jun 202512 minutes to read

This guide provides step-by-step instructions for integrating and using Syncfusion’s Smart Components with Gemini AI services in your Blazor App.

Prerequisites

Before you begin, ensure you have:

System requirements for Blazor components
Gemini API Key - Obtain an API key from Google AI Studio

Models

For a complete list of models and their capabilities, visit the Gemini Models Documentation.

Getting Started for Gemini AI with SmartTextArea

After completing this setup, you can:

Step 1: Create a Gemini AI Service

The GeminiService class serves as the foundation for integrating Gemini AI into your Blazor application. This service manages:

API communication with Gemini endpoints
Request/response handling
Message formatting
Safety settings configuration

Implementation Steps

Create a new class file named GeminiService.cs in your project
Add the following implementation:

public class GeminiService
{
    // HTTP client configuration for optimal performance
    private static readonly Version _httpVersion = HttpVersion.Version30;
    private static readonly HttpClient HttpClient = new HttpClient(new SocketsHttpHandler
    {
        PooledConnectionLifetime = TimeSpan.FromMinutes(30),
        EnableMultipleHttp2Connections = true,
    })
    {
        DefaultRequestVersion = _httpVersion
    };

    // Configuration settings
    private const string ApiKey = "YOUR_API_KEY_HERE";
    private const string ModelName = "YOUR_MODEL_NAME"; 

    // JSON serialization settings
    private static readonly JsonSerializerOptions JsonOptions = new()
    {
        PropertyNamingPolicy = JsonNamingPolicy.CamelCase
    };

    public GeminiService()
    {
        // Set up authentication headers
        HttpClient.DefaultRequestHeaders.Clear();
        HttpClient.DefaultRequestHeaders.Add("x-goog-api-key", ApiKey);
    }

    // Main method for interacting with Gemini API
    public async Task<string> CompleteAsync(IList<ChatMessage> chatMessages)
    {
        // Construct the API endpoint URL
        var requestUri = $"https://generativelanguage.googleapis.com/v1beta/models/{ModelName}:generateContent";
        
        // Prepare the request parameters
        var parameters = BuildGeminiChatParameters(chatMessages);
        var payload = new StringContent(
            JsonSerializer.Serialize(parameters, JsonOptions), 
            Encoding.UTF8, 
            "application/json"
        );

        try
        {
            // Send request and process response
            using var response = await HttpClient.PostAsync(requestUri, payload);
            response.EnsureSuccessStatusCode();

            var json = await response.Content.ReadAsStringAsync();
            var result = JsonSerializer.Deserialize<GeminiResponseObject>(json, JsonOptions);

            // Extract and return the generated text
            return result?.Candidates?.FirstOrDefault()?.Content?.Parts?.FirstOrDefault()?.Text 
                ?? "No response from model.";
        }
        catch (Exception ex) when (ex is HttpRequestException or JsonException)
        {
            throw new InvalidOperationException("Gemini API error.", ex);
        }
    }

    private GeminiChatParameters BuildGeminiChatParameters(IList<ChatMessage> messages)
    {
        // Convert chat messages to Gemini's format
        var contents = messages.Select(m => new ResponseContent(
            m.Text, 
            m.Role == ChatRole.User ? "user" : "model"
        )).ToList();

        // Configure request parameters including safety settings
        return new GeminiChatParameters
        {
            Contents = contents,
            GenerationConfig = new GenerationConfig
            {
                MaxOutputTokens = 2000,
                StopSequences = new() { "END_INSERTION", "NEED_INFO", "END_RESPONSE" }
            },
            SafetySettings = new()
            {
                new() { Category = "HARM_CATEGORY_HARASSMENT", Threshold = "BLOCK_ONLY_HIGH" },
                new() { Category = "HARM_CATEGORY_HATE_SPEECH", Threshold = "BLOCK_ONLY_HIGH" },
                new() { Category = "HARM_CATEGORY_SEXUALLY_EXPLICIT", Threshold = "BLOCK_ONLY_HIGH" },
                new() { Category = "HARM_CATEGORY_DANGEROUS_CONTENT", Threshold = "BLOCK_ONLY_HIGH" }
            }
        };
    }
}

Step 2: Define Request and Response Models

To efficiently communicate with the Gemini AI API, we need to define a set of C# classes that map to Gemini’s JSON request and response format. These models ensure type safety and provide a clean interface for working with the API.

Create a new file named GeminiModels.cs in your project
Add the following model classes:

// Represents a text segment in the API communication
public class Part
{
    public string Text { get; set; }
}

// Contains an array of text parts
public class Content
{
    public Part[] Parts { get; init; } = Array.Empty<Part>();
}

// Represents a generated response candidate
public class Candidate
{
    public Content Content { get; init; } = new();
}

// The main response object from Gemini API
public class GeminiResponseObject
{
    public Candidate[] Candidates { get; init; } = Array.Empty<Candidate>();
}

// Represents a message in the chat conversation
public class ResponseContent
{
    public List<Part> Parts { get; init; }
    public string Role { get; init; }  // "user" or "model"

    public ResponseContent(string text, string role)
    {
        Parts = new List<Part> { new Part { Text = text } };
        Role = role;
    }
}

// Configuration for text generation
public class GenerationConfig
{
    // Controls randomness (0.0 to 1.0)
    public int Temperature { get; init; } = 0;
    
    // Limits token consideration (1 to 40)
    public int TopK { get; init; } = 0;
    
    // Nucleus sampling threshold (0.0 to 1.0)
    public int TopP { get; init; } = 0;
    
    // Maximum tokens in response
    public int MaxOutputTokens { get; init; } = 2048;
    
    // Sequences that stop generation
    public List<string> StopSequences { get; init; } = new();
}

// Controls content filtering
public class SafetySetting
{
    // Harm category to filter
    public string Category { get; init; } = string.Empty;
    
    // Filtering threshold level
    public string Threshold { get; init; } = string.Empty;
}

// Main request parameters for Gemini API
public class GeminiChatParameters
{
    // Chat message history
    public List<ResponseContent> Contents { get; init; } = new();
    
    // Generation settings
    public GenerationConfig GenerationConfig { get; init; } = new();
    
    // Content safety filters
    public List<SafetySetting> SafetySettings { get; init; } = new();
}

Step 3: Create a Custom AI Service

The Syncfusion Smart Components are designed to work with different AI backends through the IChatInferenceService interface. This section shows you how to create a custom implementation that connects the Smart Components to the Gemini AI service.

Understanding the Interface

The IChatInferenceService interface is the bridge between Syncfusion Smart Components and AI services:

Create a new file named MyCustomService.cs
Add the following implementation:

using Syncfusion.Blazor.AI;

public class MyCustomService : IChatInferenceService
{
    private readonly GeminiService _geminiService;

    public MyCustomService(GeminiService geminiService)
    {
        _geminiService = geminiService;
    }

    public Task<string> GenerateResponseAsync(ChatParameters options)
    {
        // Forward the chat parameters to our Gemini service
        return _geminiService.CompleteAsync(options.Messages);
    }
}

Step 4: Configure the Blazor App

Configure your Blazor application to use the Gemini AI service with Syncfusion Smart Components. This involves registering necessary services and setting up the dependency injection container.

using Syncfusion.Blazor.SmartComponents;
using Syncfusion.Blazor.AI;
var builder = WebApplication.CreateBuilder(args);

....

builder.Services.AddSyncfusionBlazor();
builder.Services.AddSyncfusionSmartComponents();
builder.Services.AddSingleton<GeminiService>();
builder.Services.AddSingleton<IChatInferenceService, MyCustomService>();

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