Rystem.PlayFramework 9.0.0-rc.1

What is Rystem?

Play Framework

Docs

Introduction

The Rystem.PlayFramework library enables developers to integrate multi-agent systems and OpenAI into their .NET applications. It provides tools to define and orchestrate complex agent-based scenarios using OpenAI's API. In this guide, we'll cover how to install, configure, and use the UseAiEndpoints middleware and PlayFramework scenes within a .NET application.

Prerequisites

To use this library, you need:

• .NET 9.0 SDK or later
• An OpenAI API key and endpoint
• Access to an HTTP API (if you're using scenes with HTTP clients)
• A database or service to handle identity management (optional)

Installation

Follow these steps to set up Rystem.PlayFramework:

1. Clone or download the repository from GitHub.
2. Add the package to your project using the NuGet package manager:

   dotnet add package Rystem.PlayFramework --version 6.2.0
   

3. Install the required dependencies as outlined in the .csproj file:
   • Azure.AI.OpenAI
   • Azure.Identity
   • OpenAI These can be added manually via NuGet or included directly in your project�s .csproj file.

Project Setup

The .csproj file is already configured for building the package, including symbol generation and source embedding for debugging purposes. The most important parts are:

• TargetFramework: net9.0
• Package Information: Contains metadata like authorship, versioning, repository URL, and licensing.

Ensure that your csproj file contains the necessary framework and package references.

Configuration

In your .NET application, you will need to configure the services and middlewares for Rystem.PlayFramework. This is done primarily within two extension methods:

1. AddServices(IServiceCollection services, IConfiguration configuration): This method sets up the necessary services, such as OpenAI configuration, HTTP client, identity management, and PlayFramework scenes.

   Example setup:

   services.AddChat(x =>
   {
       x.AddConfiguration("openai", builder =>
       {
           builder.ApiKey = configuration["OpenAi:ApiKey"];
           builder.Uri = configuration["OpenAi:Endpoint"];
           builder.Model = configuration["OpenAi:ModelName"];
           builder.ChatClientBuilder = (chatClient) =>
           {
               chatClient.AddPriceModel(new ChatPriceSettings
               {
                   InputToken = 0.02M,
                   OutputToken = 0.02M
               });
           };
       });
   });
   
   services.AddPlayFramework(scenes =>
   {
       scenes.Configure(settings =>
       {
           settings.OpenAi.Name = "openai";
       })
       .AddScene(scene =>
       {
           scene.WithName("Weather")
               .WithDescription("Get information about the weather")
               .WithHttpClient("apiDomain")
               .WithOpenAi("openai")
               .WithApi(pathBuilder =>
               {
                   pathBuilder
                       .Map(new Regex("Country/*"))
                       .Map(new Regex("City/*"))
                       .Map("Weather/");
               })
               .WithActors(actors =>
               {
                   actors.AddActor("Ensure the country and city exist before requesting weather.")
                       .AddActor<ActorWithDbRequest>();
               });
       });
   });
   

2. UseMiddlewares(IApplicationBuilder app): This method enables middleware components for routing, authorization, HTTPS redirection, and AI endpoints. It also maps OpenAPI and Scalar API routes.

   Example middleware setup:

   app.UseRouting();
   app.UseEndpoints(endpoints =>
   {
       endpoints.MapOpenApi();
       endpoints.MapScalarApiReference();
       endpoints.MapControllers();
   });
   app.UseHttpsRedirection();
   app.UseAuthorization();
   app.UseAiEndpoints(); // Enables AI endpoints for your application
   

Configuration Options

• OpenAI Configuration: The OpenAI settings are fetched from the appsettings.json file using configuration keys such as OpenAi:ApiKey, OpenAi:Endpoint, and OpenAi:ModelName.
• HTTP Client: A named HTTP client (apiDomain) is used for interacting with external APIs.
• Scenes: You can define multiple scenes within PlayFramework, each tied to specific functionality (e.g., Weather and Identity in this example).

Example appsettings.json

{
  "OpenAi": {
    "ApiKey": "your-openai-api-key",
    "Endpoint": "https://api.openai.com",
    "ModelName": "gpt-4"
  },
  "Api": {
    "Uri": "https://api.example.com/"
  }
}

Usage

Defining a Scene

A scene in PlayFramework represents a unit of interaction. Each scene can have:

• Name: A descriptive name of the scene.
• Description: A short description of the scene's purpose.
• HttpClient: The HTTP client to use when making API requests within the scene.
• Actors: Define behaviors or actions that should be executed as part of the scene.

Example Scene: Weather

scene.WithName("Weather")
    .WithDescription("Get information about the weather")
    .WithHttpClient("apiDomain")
    .WithOpenAi("openai")
    .WithApi(pathBuilder =>
    {
        pathBuilder.Map(new Regex("Country/*"))
            .Map(new Regex("City/*"))
            .Map("Weather/");
    })
    .WithActors(actors =>
    {
        actors.AddActor("Ensure that the requested city exists, or add it with its population.")
              .AddActor<ActorWithDbRequest>(); 
    });

Example Scene: Identity Management

The IdentityManager service is used to manage user identities. It can be configured and used as follows:

scene.WithName("Identity")
    .WithDescription("Get information about the user")
    .WithOpenAi("openai")
    .WithService<IdentityManager>(builder =>
    {
        builder.WithMethod(x => x.GetNameAsync);
    });

This setup allows you to fetch information about a user, possibly using OpenAI in the process.

Middleware

The UseAiEndpoints() middleware is essential for enabling AI-powered interactions in your API. Once registered, it allows your application to respond to AI-driven requests via OpenAI or custom scenes.

How AI Endpoints Work

Endpoint Definition

The primary endpoint that gets mapped by UseAiEndpoints is api/ai/message. This endpoint receives a query string parameter m (which represents the user's message or request) and uses the ISceneManager to handle the message. The AI service or scene logic processes the message and returns a result.

Example of a GET Request:

GET https://yourdomain.com/api/ai/message?m=What%20is%20the%20weather%20today?

This will trigger the scene manager to process the message (e.g., fetching weather information using an AI service or scene logic).

Endpoint Logic

• Input: The endpoint expects a query string parameter m (message), which will be processed by the scene manager.
• Service Dependency: The ISceneManager service is injected and handles the business logic of processing the message.
• Response: The result of the scene or AI interaction is returned as a response to the client.

Authorization

The endpoints can be configured to require authorization either by default (using the isAuthorized parameter) or by specifying custom authorization policies.

Example of enforcing authorization:

app.UseAiEndpoints("AdminPolicy", "UserPolicy"); // Only users matching these policies can access AI endpoints

AI Endpoint Response (JSON Format)

When a request is made to the AI endpoint (api/ai/message), the response is returned in JSON format. The structure of the response is defined by the AiSceneResponse class.

JSON Response Structure

The JSON response will contain the following fields:

• Id: A unique identifier for the request, generated as a GUID.
• Name: The name of the scene that handled the request (optional).
• FunctionName: The name of the specific function or action executed by the scene (optional).
• Message: The original message or query that was sent to the AI endpoint.
• Arguments: Any arguments that were passed to the function (optional).
• Response: The result or output generated by the AI or scene.

Example JSON Response

{
  "Id": "123e4567-e89b-12d3-a456-426614174000",
  "Name": "Weather",
  "FunctionName": "GetWeatherInfo",
  "Message": "What is the weather in New York?",
  "Arguments": "City: New York, Country: USA",
  "Response": "The weather in New York is sunny with a temperature of 25�C."
}

Field Descriptions

• Id: A unique request identifier.
• Name: Name of the scene that handled the request, e.g., "Weather".
• FunctionName: The name of the specific function invoked by the scene, e.g., "GetWeatherInfo".
• Message: The original query made by the user, e.g., "What is the weather in New York?".
• Arguments: Any relevant parameters that were processed as part of the function, e.g., "City: New York, Country: USA".
• Response: The result of the AI's or scene's execution, e.g., "The weather in New York is sunny with a temperature of 25�C.".

This structured response allows clients to interpret and use the output of the AI or multi-agent system in a consistent manner.

Example of a weather scene

[
    {
        "id": "18b0628a-5202-47cc-813d-099702be3153",
        "name": "Weather",
        "functionName": null,
        "message": "Starting",
        "arguments": null,
        "response": null
    },
    {
        "id": "55f4754f-f372-40a2-a805-ce50c5d1c88d",
        "name": "Weather",
        "functionName": "Country_CityExists",
        "message": null,
        "arguments": "\"{\n  \u0022city\u0022: \u0022milan\u0022\n}\"",
        "response": "false"
    },
    {
        "id": "455e0384-a52e-4ee3-81d1-e0f9478a8484",
        "name": "Weather",
        "functionName": "Country_AddCity",
        "message": null,
        "arguments": "\"{\n  \u0022city\u0022: {\n    \u0022Name\u0022: \u0022milan\u0022,\n    \u0022Country\u0022: \u0022Italy\u0022,\n    \u0022Population\u0022: 1366180\n  }\n}\"",
        "response": "\"true\""
    },
    {
        "id": "aca48a7a-bb8d-4352-9d79-eb0535a2f6ed",
        "name": "Weather",
        "functionName": "Country_Exists",
        "message": null,
        "arguments": "\"{\n  \u0022country\u0022: \u0022Italia\u0022\n}\"",
        "response": "false"
    },
    {
        "id": "cdaf0ae2-3663-4287-94cf-edd1062e6bd8",
        "name": "Weather",
        "functionName": "Country_AddCountry",
        "message": null,
        "arguments": "\"{\n  \u0022country\u0022: {\n    \u0022Name\u0022: \u0022Italy\u0022,\n    \u0022Population\u0022: 60461826\n  }\n}\"",
        "response": "\"true\""
    },
    {
        "id": "0d369878-cc5f-4563-86a9-0714d909b85f",
        "name": "Weather",
        "functionName": "WeatherForecast_Get",
        "message": null,
        "arguments": "\"{\n  \u0022city\u0022: \u0022milan\u0022\n}\"",
        "response": "[{\"date\":\"2024-10-19\",\"temperatureC\":20,\"temperatureF\":67,\"summary\":\"Cool\"},{\"date\":\"2024-10-20\",\"temperatureC\":20,\"temperatureF\":67,\"summary\":\"Hot\"},{\"date\":\"2024-10-21\",\"temperatureC\":20,\"temperatureF\":67,\"summary\":\"Cool\"},{\"date\":\"2024-10-22\",\"temperatureC\":20,\"temperatureF\":67,\"summary\":\"Freezing\"},{\"date\":\"2024-10-23\",\"temperatureC\":20,\"temperatureF\":67,\"summary\":\"Hot\"}]"
    },
    {
        "id": "13192a15-e4c6-44d4-aec9-d38253c2dfe4",
        "name": "Weather",
        "functionName": null,
        "message": "Il tempo oggi a Milano � fresco con una temperatura di 20 gradi Celsius (67 gradi Fahrenheit).",
        "arguments": null,
        "response": null
    }
]

Building and Testing

To build the project, run:

dotnet build

To run tests, add a test project or directly execute requests against your API to validate the scene and actor configurations.

Conclusion

By following this guide, you can successfully install, configure, and use Rystem.PlayFramework with OpenAI integration. The key steps are setting up the services, defining scenes, and utilizing the AI middleware to handle interactions in your .NET application.