var model = new Gpt4Model("API_KEY");
var response = await model.GenerateAsync("Hello, World of AI!");

var numberOfTokens = model.CountTokens("Hello, World of AI!");

var model = new Gpt4Model("API_KEY");

var template = "What is a good name for a company that makes {product}?";
var prompt = new PromptTemplate(new PromptTemplateInput(template, new List<string>(1){"product"}));

var chain = new LlmChain(new LlmChainInput(model, prompt));

var result = await chain.Call(new ChainValues(new Dictionary<string, object>(1)
{
    { "product", "colourful socks" }
}));
// The result is an object with a `text` property.
Console.WriteLine(result.Value["text"]);

[OpenAiFunctions]
public interface IWeatherFunctions
{
    [Description("Get the current weather in a given location")]
    public Task<Weather> GetCurrentWeatherAsync(
        [Description("The city and state, e.g. San Francisco, CA")] string location,
        Unit unit = Unit.Celsius,
        CancellationToken cancellationToken = default);
}

public class WeatherService : IWeatherFunctions
{
    public Task<Weather> GetCurrentWeatherAsync(string location, Unit unit = Unit.Celsius, CancellationToken cancellationToken = default)
    {
        return Task.FromResult(new Weather
        {
            Location = location,
            Temperature = 22.0,
            Unit = unit,
            Description = "Sunny",
        });
    }
}

using var client = new HttpClient();
var model = new Gpt35TurboModel(apiKey, client);

var service = new WeatherService();
model.AddGlobalFunctions(service.AsFunctions(), service.AsCalls());

var response = await model.GenerateAsync(new ChatRequest(
    Messages: new []
    {
        "You are a helpful weather assistant.".AsSystemMessage(),
        "What's the weather like today?".AsHumanMessage(),
        "Sure! Could you please provide me with your location?".AsAiMessage(),
        "Dubai, UAE".AsHumanMessage(),
    }));

Console.WriteLine(response.Messages.AsHistory());