Gemini AI Integration with .NET MAUI Smart Components

19 Dec 20259 minutes to read

The Syncfusion .NET MAUI AI-powered components can enhance applications with intelligent capabilities. By default, it works with providers like OpenAI or Azure OpenAI, but you can integrate Google Gemini AI using the IChatInferenceService interface. This guide explains how to implement and register Gemini AI for the Smart Text Editor in a .NET MAUI app.

Setting Up Gemini

1. Get a Gemini API Key
   Visit Google AI Studio, sign in, and generate an API key.
2. Review Model Details
   Refer to Gemini Models Documentation for details on available models.

Define Request and Response Models

Create a file named GeminiModels.cs in the Services folder and add:

GeminiModels.cs

public class Part { public string Text { get; set; } }
public class Content { public Part[] Parts { get; init; } = Array.Empty<Part>(); }
public class Candidate { public Content Content { get; init; } = new(); }
public class GeminiResponseObject { public Candidate[] Candidates { get; init; } = Array.Empty<Candidate>(); }

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

public class GenerationConfig
{
    public int MaxOutputTokens { get; init; } = 2048;
    public List<string> StopSequences { get; init; } = new();
}

public class SafetySetting
{
    public string Category { get; init; } = string.Empty;
    public string Threshold { get; init; } = string.Empty;
}

public class GeminiChatParameters
{
    public List<ResponseContent> Contents { get; init; } = new();
    public GenerationConfig GenerationConfig { get; init; } = new();
    public List<SafetySetting> SafetySettings { get; init; } = new();
}

Create a Gemini AI Service

Create a service class to handle Gemini API calls, including authentication, request/response handling, and safety settings.

1. Create a Services folder in your MAUI project.
2. Add a new file named GeminiService.cs in the Services folder.
3. Implement the service as shown below:

GeminiService.cs

using System.Net;
using System.Text;
using System.Text.Json;
using Microsoft.Extensions.AI;

public class GeminiService
{
    private readonly string _apiKey = "";
    private readonly string _modelName = "gemini-2.0-flash"; // Example model
    private readonly string _endpoint = "https://generativelanguage.googleapis.com/v1beta/models/";
    private static readonly HttpClient HttpClient = new(new SocketsHttpHandler
    {
        PooledConnectionLifetime = TimeSpan.FromMinutes(30),
        EnableMultipleHttp2Connections = true
    })
    {
        DefaultRequestVersion = HttpVersion.Version20
    };

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

    public GeminiService()
    {
        HttpClient.DefaultRequestHeaders.Clear();
        HttpClient.DefaultRequestHeaders.Add("x-goog-api-key", _apiKey);
    }

    public async Task<string> CompleteAsync(List<ChatMessage> chatMessages)
    {
        var requestUri = $"{_endpoint}{_modelName}:generateContent";
        var parameters = BuildGeminiChatParameters(chatMessages);
        var payload = new StringContent(JsonSerializer.Serialize(parameters, JsonOptions), Encoding.UTF8, "application/json");

        try
        {
            using var response = await HttpClient.PostAsync(requestUri, payload);
            response.EnsureSuccessStatusCode();
            var json = await response.Content.ReadAsStringAsync();
            var result = JsonSerializer.Deserialize<GeminiResponseObject>(json, JsonOptions);
            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(List<ChatMessage> messages)
    {
        var contents = messages.Select(m => new ResponseContent(m.Text, m.Role == ChatRole.User ? "user" : "model")).ToList();
        return new GeminiChatParameters
        {
            Contents = contents,
            GenerationConfig = new GenerationConfig
            {
                MaxOutputTokens = 2000,
                StopSequences = new List<string> { "END_INSERTION", "NEED_INFO", "END_RESPONSE" }
            },
            SafetySettings = new List<SafetySetting>
        {
            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" },
            new() { Category = "HARM_CATEGORY_DANGEROUS_CONTENT", Threshold = "BLOCK_ONLY_HIGH" },
        }
        };
    }
}

Implement IChatInferenceService

Create GeminiInferenceService.cs:

GeminiInferenceService.cs

using Syncfusion.Maui.SmartComponents;

public class GeminiInferenceService : IChatInferenceService
{
    private readonly GeminiService _geminiService;

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

    public async Task<string> GenerateResponseAsync(List<ChatMessage> chatMessages)
    {
        return await _geminiService.CompleteAsync(chatMessages);
    }
}

Register Services in MAUI

Update MauiProgram.cs:

MauiProgram.cs

using Syncfusion.Maui.Core.Hosting;
using Syncfusion.Maui.SmartComponents;

var builder = MauiApp.CreateBuilder();
builder
    .UseMauiApp<App>()
    .ConfigureSyncfusionCore();

builder.Services.AddSingleton<GeminiService>();
builder.Services.AddSingleton<IChatInferenceService, GeminiInferenceService>();