dotnet add package OpenAI

using OpenAI;
using OpenAI.Chat;
using System.ClientModel;
using System.ClientModel.Primitives;

public static class Portkey
{
    private class HeaderPolicy : PipelinePolicy
    {
        private readonly Dictionary<string, string> _headers;
        public HeaderPolicy(Dictionary<string, string> headers) => _headers = headers;

        public override void Process(PipelineMessage message, IReadOnlyList<PipelinePolicy> pipeline, int index)
        {
            foreach (var header in _headers) message.Request.Headers.Set(header.Key, header.Value);
            if (index < pipeline.Count) pipeline[index].Process(message, pipeline, index + 1);
        }

        public override ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList<PipelinePolicy> pipeline, int index)
        {
            Process(message, pipeline, index);
            return ValueTask.CompletedTask;
        }
    }

    public static ChatClient CreateChatClient(Dictionary<string, string> headers, string model)
    {
        var options = new OpenAIClientOptions { Endpoint = new Uri("https://api.portkey.ai/v1") };
        options.AddPolicy(new HeaderPolicy(headers), PipelinePosition.PerCall);
        return new OpenAIClient(new ApiKeyCredential("dummy"), options).GetChatClient(model);
    }
}

public class Program
{
    public static void Main()
    {
        var client = Portkey.CreateChatClient(
            headers: new Dictionary<string, string> {
                { "x-portkey-api-key", "PORTKEY API KEY" },
                { "x-portkey-provider", "@azure-openai-prod" }, // Provider slug from Model Catalog
                { "x-portkey-trace-id", "dotnet" }
            },
            model: "gpt-4o" // Model name configured in your Azure deployment
        );

        Console.WriteLine(client.CompleteChat(new UserChatMessage("1729")).Value.Content[0].Text);
    }
}

using OpenAI;
using OpenAI.Embeddings;
using System.ClientModel;
using System.ClientModel.Primitives;

public static class PortkeyClient
{
    private class HeaderPolicy : PipelinePolicy
    {
        private readonly Dictionary<string, string> _headers;
        public HeaderPolicy(Dictionary<string, string> headers) => _headers = headers;

        public override void Process(PipelineMessage message, IReadOnlyList<PipelinePolicy> pipeline, int index)
        {
            foreach (var header in _headers) message.Request.Headers.Set(header.Key, header.Value);
            if (index < pipeline.Count) pipeline[index].Process(message, pipeline, index + 1);
        }

        public override ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList<PipelinePolicy> pipeline, int index)
        {
            Process(message, pipeline, index);
            return ValueTask.CompletedTask;
        }
    }

    public static EmbeddingClient CreateEmbeddingClient(Dictionary<string, string> headers, string model)
    {
        var options = new OpenAIClientOptions { Endpoint = new Uri("https://api.portkey.ai/v1") };
        options.AddPolicy(new HeaderPolicy(headers), PipelinePosition.PerCall);
        return new OpenAIClient(new ApiKeyCredential("dummy"), options).GetEmbeddingClient(model);
    }
}

class Program
{
    static void Main()
    {
        // Define Portkey headers
        var headers = new Dictionary<string, string> {
            // Required headers
            { "x-portkey-api-key", "..." },           // Your Portkey API key
            { "x-portkey-provider", "@openai-prod" }, // Provider slug from Model Catalog

            // Optional headers
            { "x-portkey-trace-id", "..." },          // Custom trace identifier
            { "x-portkey-config", "..." },            // Send Config ID
            // Add any other Portkey headers as needed
        };

        // Create embedding client through Portkey
        var client = PortkeyClient.CreateEmbeddingClient(
            headers: headers,
            model: "text-embedding-3-large"
        );

        // Text that we want to embed
        string description = "Best hotel in town if you like luxury hotels. They have an amazing infinity pool, a spa,"
            + " and a really helpful concierge. The location is perfect -- right downtown, close to all the tourist"
            + " attractions. We highly recommend this hotel.";

        // Generate embedding
        var embeddingResult = client.GenerateEmbedding(description);
        var vector = embeddingResult.Value.ToFloats();

        Console.WriteLine($"Full embedding dimensions: {vector.Length}");
    }
}

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;

public class Program
{
    public static async Task Main()
    {
        var headers = new Dictionary<string, string> {
            // Required headers
            { "x-portkey-api-key", "..." },           // Your Portkey API key
            { "x-portkey-provider", "@openai-prod" }, // Provider slug from Model Catalog

            // Optional headers
            // { "x-portkey-trace-id", "my-app" },    // Custom trace identifier
            // { "x-portkey-config", "..." },         // Send Config ID
            // Add any other Portkey headers as needed
        };

        // Create client
        var client = PortkeyClient.CreateClient(headers);

        var builder = Kernel.CreateBuilder().AddOpenAIChatCompletion("gpt-4", client);
        Kernel kernel = builder.Build();
        var chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();

        var history = new ChatHistory();

        // Initiate a back-and-forth chat
        string? userInput;
        do {
            // Collect user input
            Console.Write("User > ");
            userInput = Console.ReadLine();

            // Add user input
            history.AddUserMessage(userInput);

            // Get the response from the AI
            var result = await chatCompletionService.GetChatMessageContentAsync(
                history,
                null,
                kernel: kernel);

            // Print the results
            Console.WriteLine("Assistant > " + result);

            // Add the message from the agent to the chat history
            history.AddMessage(result.Role, result.Content ?? string.Empty);
        } while (userInput is not null);
    }
}