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@app.post("/agent/execute")
async def execute_agent(request: AgentRequest):
    # Framework-independent logic
    return await process_agent_request(request)

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// BundleConfig.cs (for older versions)
// Use third-party libraries like WebOptimizer for .NET Core

public static void RegisterBundles(BundleCollection bundles)
{
    bundles.Add(new ScriptBundle("~/bundles/main").Include(
                "~/wwwroot/js/*.js"));
    bundles.Add(new StyleBundle("~/Content/css").Include(
              "~/wwwroot/css/*.css"));
}

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// Service Example
public class ProductService
{
    private readonly IMemoryCache _cache;
    private readonly DataContext _context;

    public ProductService(IMemoryCache cache, DataContext context)
    {
        _cache = cache;
        _context = context;

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<img src="image.jpg" loading="lazy" alt="Description">

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// Startup.cs
public void ConfigureServices(IServiceCollection services)
{
    services.AddResponseCompression(options =>
    {
        options.MimeTypes = new[] { "image/jpeg", "image/png", "image/gif" };
    });
}

public void Configure(IApplicationBuilder app)

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<!-- View Example -->
<a href="/products/details/1" onmouseover="prefetch('/products/details/1')">Product Details</a>

<script>
function prefetch(url) {
    fetch(url, { method: 'GET', headers: { 'X-Prefetch': 'true' } });
}
</script>

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// Controller Example
public class HomeController : Controller
{
    public IActionResult Index()
    {
        // Fetch data required for the view
        var model = GetData();
        return View(model);
    }
}

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public async Task<List<string>> SemanticSearch(string query, List<string> documents)
{
    // This is a simplified example. In practice, you would use an AI service that provides semantic search.
    var embeddings = await GetEmbeddings(query);
    // Compare embeddings with document embeddings to find the most relevant documents
    // Return a list of matched documents
}

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public async Task<string> TranslateText(string text, string targetLanguage)
{
    var requestBody = new
    {
        q = text,
        target = targetLanguage,
        format = "text"
    };

    var httpContent = new StringContent(JsonConvert.SerializeObject(requestBody), System.Text.Encoding.UTF8, "application/json");

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using System;
using System.Net.Http;
using System.Threading.Tasks;
using Newtonsoft.Json;

public class ChatGPTService
{
    private readonly string apiKey = "YOUR_OPENAI_API_KEY";
    private readonly HttpClient client;