Alexa.Tip – Build Intent Handlers in .NET

In this Alexa.Tip series, we explore some little bits of code that can make your life easier in developing Alexa Skills in many languages including C# + .NET, node.jS + TypeScript, Kotlin, etc. We look at concepts that developers might not be aware of, design patterns that and how they can be applied to voice application development, best practices, and more!

In this post, we explore some more best practices in developing Alexa Skills in C# whether you are using an ASP.NET Core API or an AWS Lambda. This time, we talk about abstracting our business logic out of either our Function or Controller and into separate Handler classes.

If you’ve done skill development in node.js or in .NET, you’ve probably noticed that a lot of docs, example apps, and real world apps are written with one BIG class or file. That’s pretty gross.

You may some C# examples look like this:

UglyFunction.cs

public class Function
{
    public async Task<SkillResponse> HandleRequest(SkillRequest input)
    {
        if(input.GetRequestType() == typeof(LaunchRequest))
        {
            return ResponseBuilder.Ask("Welcome to my skill written in one big function class with all my business logic in one place and no real testability! Ask me anything!", null);
        }

        if(input.GetRequestType() == typeof(IntentRequest))
        {
            var intentRequest = input.Request as IntentRequest;

            switch(intentRequest.Intent.Name)
            {
                case "MyIntent1":
                    // get slot values
                    // search database
                    // validate data
                    // build response 
                    // add card if it supports a screen
                    // return
                    break;
                case "Intent2":
                    // get slot values
                    // search third party api
                    // validate data
                    // build response 
                    // add card if it supports a screen
                    // return
                    break;
                case "Intent3":
                    // get slot values
                    // search database
                    // validate data
                    // build response 
                    // add card if it supports a screen
                    // return
                    break;
                case "Intent4":
                    // get slot values
                    // search third party api
                    // validate data
                    // build response 
                    // add card if it supports a screen
                    // return
                    break;
                case "Intent5":
                    // get slot values
                    // search database
                    // validate data
                    // build response 
                    // add card if it supports a screen
                    // return
                    break;

                // I think you get my point here...
            }
        }

        return ResponseBuilder.Tell("Something went wrong. Please try again later");
    }
}

So that’s pretty gross right? It’s untestable, difficult to update, and hard to read! But hey, we’re C# developers. So let’s build Alexa Skills like C# developers.

The proposal here is to abstract your intent logic into Handler classes. Then you can inject those either into your Function or your Controller if you’re using a RESTful API.

Here’s something that I hope can get you started:
– A generic IIntentHandler
– An inherited specific IIntentWhateverHandler
– An implementation of the IIntentWhateverHandler
– A testable and injectable version to be used in the function.

Let’s start.

IIntentHandler.cs

public interface IIntentHandler
{
    Task<SkillResponse> HandleIntent(IntentRequest input);
}

Now let’s get specific. In this simple case, this interface is empty but exists for registration – although you can add specific methods here as needed.

IMyIntentHandler.cs

public interface IMyIntentHandler : IIntentHandler
{
}

Now let’s create an example implementation of an intent handler that houses the business logic of the request.

MyIntentHandler.cs

public class MyIntentHandler : IMyIntentHandler
{
    private readonly MyDbContext _context;
    public MyIntentHandler(MyDbContext context)
    {
        // oh snap, we can inject db context
        _context = context;
    }

    public async Task<SkillResponse> HandleIntent(IntentRequest input)
    {
        var mySlot = input.Slots["MySlot"].Value; // assumes the slot is there from it being required in the interaction model
        var myMessage = await _context.Messages.FirstOrDefaultAsync(m => m.SomeValue == mySlot)

        if(myMessage == null)
        {
            return ResponseBuilder.Ask("I don't know that one. Try something else.");
        }

        return ResponseBuilder.Tell(myMessage.Content);
    }
}

Now let’s go about adding these handlers to our Function (the same thing can apply to our Controllers.

PrettyFunction.cs

public class Function
{
    private readonly IMyIntentHandler _myIntentHandler;

    private void Setup()
    {
        _myIntentHandler = new MyIntentHandler(...);
        // create the others - optionally implement a ServiceCollection
        // to handle proper dependency injection
    }
    public async Task<SkillResponse> HandleRequest(SkillRequest input)
    {
        if(input.GetRequestType() == typeof(LaunchRequest))
        {
            return ResponseBuilder.Ask("Welcome to my skill written in one big function class with all my business logic in one place and no real testability! Ask me anything!", null);
        }

        if(input.GetRequestType() == typeof(IntentRequest))
        {
            var intentRequest = input.Request as IntentRequest;

            switch(intentRequest.Intent.Name)
            {
                case "MyIntent1":
                    return await _myIntentHandler.HandleIntent(intentRequest);
                    break;
            }
        }

        return ResponseBuilder.Tell("Something went wrong. Please try again later");
    }
}

So now you can separately test your Handler classes, your Data classes, and your Function as a whole.

SO much better šŸ™‚

Conclusion

We don’t have to follow how Amazon writes their node.js skills when we write them in C# – let’s use some proper OO design and testability to build some scalable and awesome skills!

In the next post, we’ll talk about taking this one step closer to avoid that gross switch statement and simply register our Handler implementations for certain intents or RequestTypes which is more similar to how the actual ASK SDK works.

Stay tuned!


If you like what you see, donā€™t forget to follow me on twitter @Suave_Pirate, check out my GitHub, and subscribe to my blog to learn more mobile and AI developer tips and tricks!

Interested in sponsoring developer content? MessageĀ @Suave_PirateĀ on twitter for details.


voicify_logo
I’m the Director and Principal Architect over at Voicify. Learn how you can use the Voice Experience Platform to bring your brand into the world of voice on Alexa, Google Assistant, Cortana, chat bots, and more: https://voicify.com/


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Alexa.Tip – Using Entity Framework in Your C# Alexa Skill Lambda

In this Alexa.Tip series, we explore some little bits of code that can make your life easier in developing Alexa Skills in many languages including C# + .NET, node.jS + TypeScript, Kotlin, etc. We look at concepts that developers might not be aware of, design patterns that and how they can be applied to voice application development, best practices, and more!

In the previous Alexa.Tip, we explored Accessing Lambda Environment Variables in .NET in order to access a secure connection string and initialize a DbContext to use.

If you don’t know how to access Lambda Environment variables already, PLEASE read that post first as this post’s samples will NOT show the use of hard coded connection strings – let’s follow some best practices! šŸ˜€

Alright, let’s get into it. If you’re developing Alexa Skills in .NET, you probably need to access data somewhere! At least if you’re building a real-world scale voice application. Also, if you’re a .NET developer, you’re probably used to accessing data through Entity Framework at this point rather than using some of the AWS SDKs and data services such as DynamoDB.

It’s super easy…

Initial Skill

First off, this example uses a super basic skill where the only custom intent is to get facts about animals. Here’s the intent setup in the UI:
AnimalFactIntent_Full

You can also find the interactionModel.json here:
https://github.com/SuavePirate/Cross-Platform-Voice-NET/blob/master/src/End/AnimalFacts/AnimalFacts/InteractionModels/alexaInteractionModel.json

Let’s start by abstracting some handlers out so we aren’t writing everything in our Function class – this isn’t JavaScript after all:

WelcomeHandler.cs

public class WelcomeHandler : IResponseHandler
{
    public SkillResponse GetResponse(SkillRequest input)
    {
        return ResponseBuilder.Ask("Welcome to the animal facts voice app! You can ask me about all kinds of animals - try saying, tell me about dogs.");
    }
}

AnimalFactHandler.cs

public class AnimalFactHandler : IAsyncResponseHandler
{
    public async Task<SkillResponse> GetResponse(SkillRequest input)
    {
        // TODO: Do something with data to get the fact.
    }    
}

Then we can setup our Function:

Function.cs

public class Function
{
    // using these interfaces can help us use something like Moq to unit test this bad boy.
    private readonly IResponseHandler _welcomeHandler;
    private readonly IAsyncResponseHandler _animalFactHandler;

    public Function() 
    {
        _welcomeHandler = new WelcomeHandler();
        _animalFactHandler = new AnimalFactHandler();
    }  

    public async Task<SkillResponse> FunctionHandler(SkillRequest input, ILambdaContext context)
    {
        var requestType = input.GetRequestType();

        if (requestType == typeof(IntentRequest))
        {
            if ((input.Request as IntentRequest).Intent.Name == "AnimalFactIntent")
            { 
                return await _animalFactHandler.GetResponse(input);
            }
        }
        else if (requestType == typeof(Alexa.NET.Request.Type.LaunchRequest))
        {
            return _welcomeHandler.GetResponse(input);
        }    

        return ResponseBuilder.Ask("I'm not sure I know how to do that. Try asking me for an animal fact!");
    }
}

So we have a basic setup, but we need to actually implement something in the AnimalFactHandler. In this case, we want to do a SQL search for a fact about the animal that was asked about!

SO let’s start wiring up Entity Framework to this skill:

Add Models and Context

Let’s create a model for our AnimalFact entity to represent our table:

AnimalFact.cs

public class AnimalFact
{
    public int Id { get; set; }
    public string AnimalName { get; set; }
    public string Fact { get; set; }
}

And now let’s create a DbContext implementation with our AnimalFact:

AnimalContext

public class AnimalContext : DbContext
{
    public DbSet<AnimalFact> AnimalFacts { get; set; }
    public AnimalContext(DbContextOptions options)
        : base(options)
    {

    }

    public AnimalContext()
    {

    }
}

NOTE: If you are NOT also using this AnimalContext in a web app that handles the migrations, you’ll need to add either a web app or a console project with .NET Core to handle the migrations since it needs to be an executable project type. The assumption here is that you already have a project running these migrations so you can actually add data to it, but if you are starting here, simply create a new .NET Core web app project or Console app, reference your DbContext from a shared library between the Labmda project and the app project, then run your migrations from the app project.

From here on out, we assume you have the db created from your context and some data in it.

Updating the Skill Lambda

Now that we have a DbContext, let’s add it to our Function and AnimalFactHandler in order to search for the fact!

Let’s go to the AnimalFactHandler and setup the class to take the AnimalContext in the constructor so we can easily unit test and then use the slot value from the request to query for the animal:

AnimalFactHandler.cs

public class AnimalFactHandler : IAsyncResponseHandler
{
    private readonly AnimalContext _context;
    public AnimalFactHandler(AnimalContext context)
    {
        _context = context;
    }

    public async Task<SkillResponse> GetResponse(SkillRequest input)
    {
        // get the animal name from the slot
        var intent = (input.Request as IntentRequest).Intent;
        var animal = intent.Slots["Animal"].Value;

        // query for the fact
        var animalFact = _context.AnimalFacts
                            .Where(a => a.AnimalName.ToLower() == animal.ToLower())
                            .FirstOrDefaultAsync();

        // we found the animal!
        if (animalFact != null)
        {
            return ResponseBuilder.Tell(animalFact.Fact);
        }

        // couldn't find the animal - return a decent response
        return ResponseBuilder.Ask("I don't know about that animal. Try asking me about a different one!");
    }    
}

Now that we have the updated handler, let’s update the entry point to initialize and pass in the AnimalContext.

Function.cs

public class Function
{
    // using these interfaces can help us use something like Moq to unit test this bad boy.
    private readonly IResponseHandler _welcomeHandler;
    private readonly IResponseHandler _animalFactHandler;
    private readonly AnimalContext _context;

    public Function() 
    {
        var connectionString = Environment.GetEnvironmentVariable("DatabaseConnectionString");
        var timeoutSetting = Environment.GetEnvironmentVariable("DatabaseCommandTimeout");
        var optionsBuilder = new DbOptionsBuilder<AnimalFactContext>()
            .UseSqlServer(connectionString, providerOptions => providerOptions.CommandTimeout(int.Parse(timeoutSetting)));
        _context = new AnimalFactContext(optionsBuilder.Build());
        _welcomeHandler = new WelcomeHandler();
        _animalFactHandler = new AnimalFactHandler(_context);
    }  

    public SkillResponse FunctionHandler(SkillRequest input, ILambdaContext context)
    {
        var requestType = input.GetRequestType();

        if (requestType == typeof(IntentRequest))
        {
            if ((input.Request as IntentRequest).Intent.Name == "AnimalFactIntent")
            { 
                return _animalFactHandler.GetResponse(input);
            }
        }
        else if (requestType == typeof(Alexa.NET.Request.Type.LaunchRequest))
        {
            return _welcomeHandler.GetResponse(input);
        }    

        return ResponseBuilder.Ask("I'm not sure I know how to do that. Try asking me for an animal fact!");
    }
}

Now we can publish this and check out our results!

Results

For example, I’ll use Swagger to create an animal fact, and then ask for it!

Boom – created:
create_dog_fact.PNG

And now let’s ask for it!

dog_fact_response

Conclusion

Adding data-driven responses to your custom Alexa Skills is dead easy using techniques you may already be familiar with if you are a .NET web developer! Show me what you’ve built with .NET and Alexa in the comments!


If you like what you see, donā€™t forget to follow me on twitter @Suave_Pirate, check out my GitHub, and subscribe to my blog to learn more mobile and AI developer tips and tricks!

Interested in sponsoring developer content? MessageĀ @Suave_PirateĀ on twitter for details.


voicify_logo
I’m the Director and Principal Architect over at Voicify. Learn how you can use the Voice Experience Platform to bring your brand into the world of voice on Alexa, Google Assistant, Cortana, chat bots, and more: https://voicify.com/


Alexa.Tip – Build Better Responses in .NET With SSML

In this Alexa.Tip series, we explore some little bits of code that can make your life easier in developing Alexa Skills in many languages including C# + .NET, node.jS + TypeScript, Kotlin, etc. We look at concepts that developers might not be aware of, design patterns that and how they can be applied to voice application development, best practices, and more!

In this post, we are looking at a VERY simple, but useful tool when developing Alexa Skills – SSML or Speech Synthesis Markup Language.

If you’re new to voice application development, SSML is a common standard among all the big players – Alexa, Google, Cortana, etc. At its simplest form, it’s a simple set of XML elements that are used to tell the Voice Assistant (such as Alexa) how to speak your response in a particular way. The alternative to using SSML is to return simple text as a string and let the assistant respond with their normal speech. However, this commonly falls short on things like:

  • Abbreviations
  • Dates
  • Emphasis
  • Pauses
  • Tone
  • Pitch

This is especially true when the assistant is reading out a particularly long set of text. It becomes too… robotic. Ironically…

So let’s look at an example of a C# and .NET response using simple text:

Function.cs

public SkillResponse SimpleTextResponse()
{
    return ResponseBuilder.Tell(new PlainTextOutputSpeech
    {
        Text = "When one door of happiness closes, another opens; but often we look so long at the closed door that we do not see the one which has been opened for us."
    });
}

With a response like this, Alexa will likely read it too quickly which can really diminish the power behind such a great quote (by Helen Keller).

Now we can very easily update a response like this to give ourselves something more powerful:

Function.cs

public SkillResponse RichTextResponse()
{
    return ResponseBuilder.Tell(new SsmlOutputSpeech
    {
        Ssml = 
            @"<speak>
                When <emphasis level=""moderate"">one</emphasis> door of happiness closes, another opens;
                <break time=""1.5s""/>
                <s>but often we look so long at the <emphasis level=""strong"">closed</emphasis> door that we do not see the one which has been opened for us.</s>
            </speak>"
    });
}

Now with this, we can add emphasis of different levels on certain words, add breaks in the speech when we need to, and separate contextual sentences!

For a full list of SSML tags that Alexa supports check this out:
https://developer.amazon.com/docs/custom-skills/speech-synthesis-markup-language-ssml-reference.html

And be sure to check back for more intro to expert level tips on Alexa Skill development!
Or take a look at other posts in the Alexa.Tip series here!

Side note: I’m also working on a tool to make building SSML responses even better – especially for very large and dynamic pieces of conversations. The idea is to build a more Fluid Design approach for something like this:

// returns a string
return SsmlBuilder.Speak("When")
    .Emphasis("one", Emphasis.Moderate)
    .Speak("door of hapiness closes, another opens;)
    .Break(1.5)
    .Sentence("but often we look so long at the closed door that we do not see the one which has been opened for us.")
    .Build();

Or something like:

// returns a string - uses string searching
return SsmlBuilder.Speak("When one door of happiness closes, another opens; but often we look so long at the closed door that we do not see the one which has been opened for us.")
    .Emphasis("one", Emphasis.Moderate)
    .Break(";")
    .Sentence("but often we look so long at the closed door that we do not see the one which has been opened for us.")
    .Emphasis("closed", Emphasis.Strong)
    .Build();

Or:

// returns a string - uses indexes and lengths
return SsmlBuilder.Speak("When one door of happiness closes, another opens; but often we look so long at the closed door that we do not see the one which has been opened for us.")
    .Emphasis(4, 3, Emphasis.Moderate)
    .Break(47)
    .Sentence(49, 112)
    .Emphasis(59, 6, Emphasis.Strong)
    .Build();

Let me know what you think in the comments or on twitter @Suave_Pirate – Would you rather build SSML Responses like this or is writing the XML in a big fat string working alright for you?


If you like what you see, donā€™t forget to follow me on twitter @Suave_Pirate, check out my GitHub, and subscribe to my blog to learn more mobile and AI developer tips and tricks!

Interested in sponsoring developer content? MessageĀ @Suave_PirateĀ on twitter for details.


voicify_logo
I’m the Director and Principal Architect over at Voicify. Learn how you can use the Voice Experience Platform to bring your brand into the world of voice on Alexa, Google Assistant, Cortana, chat bots, and more: https://voicify.com/


Alexa.Tip – How to Host Alexa Skills in Azure

If you’re a .NET developer in the Azure cloud sphere and want to start dabbling in Alexa Skill Development, you don’t have to uproot your entire career and development process to go learn AWS, how Lambdas work, the serverless lifecycle, etc. In this post, we’ll go over just how easy it can be to build an Alexa Skill using C#, ASP.NET Core, and Azure!

The traditional setup for Alexa Skills is:
– AWS Lambda
– .NET Core Lambda project template

But we’re going to be using:
– Azure App Services
– ASP.NET Core API project template

NOTE: This is not an introduction into how Alexa skills work, or how to create your very first skill. This is meant to provide options for hosting your skills, and for how to build skills in general using .NET. If you’re BRAND new to Alexa Skill development, start by reading through their documentation (mostly using node.js, but focus on the structure of an interaction model, what intents and slots are, etc.)

For source of the entire project checkout: https://github.com/SuavePirate/Alexa.NET.WebApi

Step 1 – Create an Alexa Developer Account

If you haven’t already created an Alexa Developer account, you’ll need one in order to register and test your skill.

Simply follow, the Amazon documentation on how to do this – super simple: https://developer.amazon.com/alexa

Step 2 – Create a Skill in the Developer Console

With your Alexa developer account, create a new Skill.

Choose Custom Skill:

create_skill

Then choose the empty “Start from Scratch” template:

choose_template

Now you can manage your interaction model.

For this example, we are going to create a skill that let’s users ask for information about Animals. We’ll use a custom Intent for this, but use the built in AMAZON.Animal slot.

On the left side, go to JSON Editor and paste this interaction model. You can make adjustments as you see fit:

interactionModel.json

{
  "interactionModel": {
    "languageModel": {
      "invocationName": "animal facts",
      "intents": [
        {
          "name": "AMAZON.FallbackIntent",
          "samples": []
        },
        {
          "name": "AMAZON.CancelIntent",
          "samples": [
            "I'm all set"
          ]
        },
        {
          "name": "AMAZON.HelpIntent",
          "samples": []
        },
        {
          "name": "AMAZON.StopIntent",
          "samples": [
            "Quit",
            "Goodbye"
          ]
        },
        {
          "name": "AnimalFactIntent",
          "slots": [
            {
              "name": "Animal",
              "type": "AMAZON.Animal"
            }
          ],
          "samples": [
            "I'd like to know about {Animal}",
            "What is an {Animal}",
            "Give me information about the {Animal}",
            "Tell me about {Animal}"
          ]
        }
      ],
      "types": []
    }
  }
}

This allows our users to ask things such as “Tell me about Monkeys” and then use the animal they are asking for to send information back to them!

Step 3 – Create the ASP.NET Core API

In Visual Studio, create a new project and choose the ASP.NET Core Web Application:

aspnetcore

In the next dialog, select the API template, and be sure that HTTPS is enabled:

API_project_with_https

With your new web application, you’ll need to install the Alexa.NET Nuget Package from Tim Heuer
https://www.nuget.org/packages/Alexa.NET/1.5.5-pre

This nuget package has models for the JSON that Alexa sends to your API and a convenient fluid API design for building your responses that is similar to how the actual Alexa Java and JavaScript SDKs work.

Now that the Alexa models are available, let’s create a new API controller:

AlexaController.cs

[Route("api/[controller]")]
public class AlexaController : Controller
{
    /// <summary>
    /// This is the entry point for the Alexa skill
    /// </summary>
    /// <param name="input"></param>
    /// <returns></returns>
    [HttpPost]
    public SkillResponse HandleResponse([FromBody]SkillRequest input)
    {
        var requestType = input.GetRequestType();

        // return a welcome message
        if(requestType == typeof(LaunchRequest))
        {
            return ResponseBuilder.Ask("Welcome to animal facts, ask me about information on an animal", null);
        }

        // return information from an intent
        else if (requestType == typeof(IntentRequest))
        {
            // do some intent-based stuff
            var intentRequest = input.Request as IntentRequest;

            // check the name to determine what you should do
            if (intentRequest.Intent.Name.Equals("AnimalFactIntent"))
            {
                // get the slots
                var animal = intentRequest.Intent.Slots["Animal"].Value;
                if(animal == null)
                    return ResponseBuilder.Ask("You forgot to ask about an animal! Please try again.", null);

                return ResponseBuilder.Tell($"I would normally tell you facts about ${animal} but I'm not a real skill yet.");
            }
        }

        return ResponseBuilder.Ask("I didn't understand that, please try again!", null);
    }
}

This controller contains a single HTTP POST endpoint that takes in an SkillRequest. It then breaks down the request in order to respond accordingly. So if the user opens the skill which initiates the LaunchRequest, we can give them a welcome message with hints on what they can ask about. Then if they ask for information about an animal, we can handle the IntentRequest with the intent name of “AnimalFactIntent” and use the Animal slot value!

That’s all there is too it! I’d normally suggest abstracting this out of using the controller so that it is more easily testable with unit tests, but for the sake of a simple example, it will work fine.

Step 4 – Publish to Azure App Service

Now that we have our ASP.NET Core project, we need to publish it to an Azure App Service. You can do this directly in Visual Studio or in the Azure portal. For the sake of this example, we’ll do it directly in Visual Studio – assuming you have a valid Azure license and account setup.

From the Solution Explorer – right click your project and Select “Publish”.

Select the “App Service” option and click “Create”.

Then fill out the form to create a new App Service:
AzurePublish

Once your publish is done, you’ll need to ensure that HTTPS / SSL is configured correctly for your app service.

In the Azure portal, go to your new App Service, then scroll to the SSL Settings section.

Be sure that you enforce SSL (Alexa requires endpoints to use HTTPS with trusted certificates. The default App Service SSL cert is signed with Microsoft’s trusted certificate which means that Amazon will accept it).

azure_ssl.PNG

Now make sure that you can hit your App service API endpoint. I usually prefer to enable Swagger for my API using Swashbuckle for this: https://docs.microsoft.com/en-us/aspnet/core/tutorials/getting-started-with-swashbuckle?view=aspnetcore-2.1&tabs=visual-studio%2Cvisual-studio-xml

Step 5 – Register Endpoint with Alexa Skill

Now that we have our App Service with HTTPS set up, we need to go back to the Alexa Developer Console and to the skill. In the left menu, scroll down to the “Endpoint” option.

In the main view, select “HTTPS” instead of “AWS Lambda ARN”. Then enter the URL for the endpoint in your app service and choose the subdomain ssl cert option:
azure_alexa_endpoint

Once this is done, you can save, and build your skill then proceed to test it!

Step 6 – Test

In the Alexa Developer Console, go to your skill and select the “Test” tab. Here you can type or say things like “Open Animal Facts” or “Ask Animal Facts to tell me about Monkeys”.

animal skill test

Here you can also view the JSON that Alexa is sending to your skill and use that to test against your skill directly using something like Swagger or Postman.

Here’s an example JSON input you could use to test:

{
    "version": "1.0",
    "session": {
        "new": false,
        "sessionId": "...",
        "application": {
            "applicationId": "..."
        },
        "user": {
            "userId": "..."
        }
    },
    "context": {
        "AudioPlayer": {
            "playerActivity": "IDLE"
        },
        "Display": {
            "token": ""
        },
        "System": {
            "application": {
                "applicationId": "..."
            },
            "user": {
                "userId": "..."
            },
            "device": {
                "deviceId": "...",
                "supportedInterfaces": {
                    "AudioPlayer": {},
                    "Display": {
                        "templateVersion": "1.0",
                        "markupVersion": "1.0"
                    }
                }
            },
            "apiEndpoint": "https://api.amazonalexa.com",
            "apiAccessToken": "..."
        }
    },
    "request": {
        "type": "IntentRequest",
        "requestId": "...",
        "timestamp": "2018-09-04T17:07:45Z",
        "locale": "en-US",
        "intent": {
            "name": "AnimalFactIntent",
            "confirmationStatus": "NONE",
            "slots": {
                "Animal": {
                    "name": "Animal",
                    "value": "monkeys",
                    "confirmationStatus": "NONE"
                }
            }
        }
    }
}

You can also test on a real Alexa device! Once you enabled testing, any device that is assigned to the same email address as your Alexa Developer Account will have it enabled.

And that’s it! Now get out there and build some Alexa Skills in .NET!


If you like what you see, donā€™t forget to follow me on twitter @Suave_Pirate, check out my GitHub, and subscribe to my blog to learn more mobile and AI developer tips and tricks!

Interested in sponsoring developer content? MessageĀ @Suave_PirateĀ on twitter for details.


voicify_logo
I’m the Director and Principal Architect over at Voicify. Learn how you can use the Voice Experience Platform to bring your brand into the world of voice on Alexa, Google Assistant, Cortana, chat bots, and more: https://voicify.com/


Adding a File Upload Field to Your Swagger UI With Swashbuckle

If you’re building ASP.NET Core Web APIs, then I hope you’ve heard of Swashbuckle – the tool to generate the Swagger UI automatically for all of your controllers to make manual testing your endpoints visual and simple.

Out of the box, the documentation helps you set up your UI, handle different ways to authenticate (which we will touch on in a later post), and have it all hooked up to your controllers. However, this only handles the most common cases of required requests with query string parameters and HTTP Body content.

In this post, we’ll look at a quick and easy way to also add File upload fields for your API endpoints that consume IFormFile properties to make testing file uploading even easier.

Basic Swagger Setup

First thing’s first, install that puppy:

Package Manager : Install-Package Swashbuckle.AspNetCore
CLI : dotnet add package Swashbuckle.AspNetCore

Let’s first look at a simple swagger setup as our baseline before we add everything for our HTTP Header Field.

Startup.cs

//...
public void ConfigureServices(IServiceCollection services)
{
    // ...
    services.AddSwaggerGen(config =>
    {
        config.SwaggerDoc("v1", new Info { Title = "My API", Version = "V1" });
    });
    // ...
}

public void Configure(IApplicationBuilder app, IHostingEnvironment env)
{
    // ...
    app.UseMvc();

    app.UseSwagger();
    app.UseSwaggerUI(config =>
    {
        config.SwaggerEndpoint("/swagger/v1/swagger.json", "My API v1");
    });
    // ...
}
//...

This setup gives us all we need for our basic UI and wireup to our controllers!
Running this gives us our basic swagger at /swagger:
Swagger no header

Adding a File Upload Field

What we have to do now is add an OperationFilter to our swagger generation. These OperationFilters can do a whole lot and enable us to customize the swagger document created which is what drives the fields and info on the UI.

Let’s start by creating our FormFileSwaggerFilter class.

FormFileSwaggerFilter.cs

/// <summary>
/// Filter to enable handling file upload in swagger
/// </summary>
public class FormFileSwaggerFilter : IOperationFilter
{
    private const string formDataMimeType = "multipart/form-data";
    private static readonly string[] formFilePropertyNames =
        typeof(IFormFile).GetTypeInfo().DeclaredProperties.Select(p => p.Name).ToArray();

    public void Apply(Operation operation, OperationFilterContext context)
    {
        var parameters = operation.Parameters;
        if (parameters == null || parameters.Count == 0) return;

        var formFileParameterNames = new List<string>();
        var formFileSubParameterNames = new List<string>();

        foreach (var actionParameter in context.ApiDescription.ActionDescriptor.Parameters)
        {
            var properties =
                actionParameter.ParameterType.GetProperties()
                    .Where(p => p.PropertyType == typeof(IFormFile))
                    .Select(p => p.Name)
                    .ToArray();

            if (properties.Length != 0)
            {
                formFileParameterNames.AddRange(properties);
                formFileSubParameterNames.AddRange(properties);
                continue;
            }

            if (actionParameter.ParameterType != typeof(IFormFile)) continue;
            formFileParameterNames.Add(actionParameter.Name);
        }

        if (!formFileParameterNames.Any()) return;

        var consumes = operation.Consumes;
        consumes.Clear();
        consumes.Add(formDataMimeType);

        foreach (var parameter in parameters.ToArray())
        {
            if (!(parameter is NonBodyParameter) || parameter.In != "formData") continue;

            if (formFileSubParameterNames.Any(p => parameter.Name.StartsWith(p + "."))
                || formFilePropertyNames.Contains(parameter.Name))
                parameters.Remove(parameter);
        }

        foreach (var formFileParameter in formFileParameterNames)
        {
            parameters.Add(new NonBodyParameter()
            {
                Name = formFileParameter,
                Type = "file",
                In = "formData"
            });
        }
    }
}

This operation filter takes the operation parameters, then uses reflection to find the type of the field. If the field is an IFormFile, then we add a new file field from the formData section to our parameters. This in turn will update our swagger definition json file, and when rendered adds the field to our UI.

This even works great with endpoints that take a separate HTTP Body, query parameters, and files!

Now we need to reference it in our Startup when we initialize swagger:

Startup.cs

//...
public void ConfigureServices(IServiceCollection services)
{
    // ...
    services.AddSwaggerGen(config =>
    {
        config.SwaggerDoc("v1", new Info { Title = "My API", Version = "V1" });

        config.OperationFilter<FormFileSwaggerFilter>();
    });
    // ...
}

Here’s an example controller with an endpoint that uses the file upload:

FileUploadController

[Route("api/[controller]")]
public class FileUploadController : Controller
{
    [HttpPost]
    public async Task<IActionResult> CreateMediaItem(string name, [FromForm]IFormFile file)
    {
        // Do something with the file
        return Ok();
    }
}

This controller ends up rendering in our Swagger UI as:
Fileupload_swagger

And using this, we can now submit our request with an uploaded file and all our other parameters!


If you like what you see, donā€™t forget to follow me on twitter @Suave_Pirate, check out my GitHub, and subscribe to my blog to learn more mobile developer tips and tricks!

Interested in sponsoring developer content? MessageĀ @Suave_PirateĀ on twitter for details.

Adding a Required HTTP Header to Your Swagger UI With Swashbuckle

If you’re building ASP.NET Core Web APIs, then I hope you’ve heard of Swashbuckle – the tool to generate the Swagger UI automatically for all of your controllers to make manual testing your endpoints visual and simple.

Out of the box, the documentation helps you set up your UI, handle different ways to authenticate (which we will touch on in a later post), and have it all hooked up to your controllers. However, this only handles the most common cases of required requests with query string parameters and HTTP Body content.

In this post, we’ll look at a quick and easy way to also add fields for your custom HTTP Request Headers so you can fill them out while testing without having to do some funky stuff in the console.

Basic Swagger Setup

First thing’s first, install that puppy:

Package Manager : Install-Package Swashbuckle.AspNetCore
CLI : dotnet add package Swashbuckle.AspNetCore

Let’s first look at a simple swagger setup as our baseline before we add everything for our HTTP Header Field.

Startup.cs

//...
public void ConfigureServices(IServiceCollection services)
{
    // ...
    services.AddSwaggerGen(config =>
    {
        config.SwaggerDoc("v1", new Info { Title = "My API", Version = "V1" });
    });
    // ...
}

public void Configure(IApplicationBuilder app, IHostingEnvironment env)
{
    // ...
    app.UseMvc();

    app.UseSwagger();
    app.UseSwaggerUI(config =>
    {
        config.SwaggerEndpoint("/swagger/v1/swagger.json", "My API v1");
    });
    // ...
}
//...

This setup gives us all we need for our basic UI and wireup to our controllers!
Running this gives us our basic swagger at /swagger:
Swagger no header

Adding Custom Headers

What we have to do now is add an OperationFilter to our swagger generation. These OperationFilters can do a whole lot and enable us to customize the swagger document created which is what drives the fields and info on the UI.

Let’s create a MyHeaderFilter and then add it to the AddSwaggerGen call.

MyHeaderFilter.cs

/// <summary>
/// Operation filter to add the requirement of the custom header
/// </summary>
public class MyHeaderFilter : IOperationFilter
{
    public void Apply(Operation operation, OperationFilterContext context)
    {
        if (operation.Parameters == null)
            operation.Parameters = new List<IParameter>();

        operation.Parameters.Add(new NonBodyParameter
        {
            Name = "MY-HEADER",
            In = "header",
            Type = "string",
            Required = true // set to false if this is optional
        });
    }
}

And now we need to update our Startup class:

Startup.cs

//...
public void ConfigureServices(IServiceCollection services)
{
    // ...
    services.AddSwaggerGen(config =>
    {
        config.SwaggerDoc("v1", new Info { Title = "My API", Version = "V1" });

        config.OperationFilter<MyHeaderFilter>();
    });
    // ...
}

Now when we run and navigate to our /swagger url, we can see:
Swagger with header

When we fill out that field, we can now pull the value off the request header in our controller!

ContentController.cs

[Route("api/[controller]")]
public class ContentController : Controller
{
    public void Search([FromBody]InputModel model)
    {
        Console.WriteLine(Request.Headers["MY-HEADER"]);        
    }
}

In further posts, we’ll talk about adding Bearer Authentication, XML Comments, and More šŸ™‚


If you like what you see, donā€™t forget to follow me on twitter @Suave_Pirate, check out my GitHub, and subscribe to my blog to learn more mobile developer tips and tricks!

Interested in sponsoring developer content? MessageĀ @Suave_PirateĀ on twitter for details.

Add Model Validation to Your ASP.NET Core Web API

If you’re used to creating traditional Web APIs in ASP.NET, you’ve probably become accustom to using System.ComponentModel.DataAnnotations such as the [Required] or [StringLength] data attributes. Using these in your models that are parameters to your API project would return the proper 400 response code when the input didn’t match the requirement.

In ASP.NET Core 2.0, this is no longer built in right out of the box, but setting it up is super easy! So if you don’t want to abstract your model validation in your business logic layer and want to get it out of the way right away when the request comes in, check this out.

There are only two steps to doing this:

  • Create an implementation of `IActionFilter`
  • Register this Filter class in your startup

Let’s start by creating a ValidatorActionFilter class:

ValidatorActionFilter.cs

/// <summary>
/// Filter for returning a result if the given model to a controller does not pass validation
/// </summary>
public class ValidatorActionFilter : IActionFilter
{
    public void OnActionExecuting(ActionExecutingContext filterContext)
    {
        if (!filterContext.ModelState.IsValid)
        {
            filterContext.Result = new BadRequestObjectResult(filterContext.ModelState);
        }
    }

    public void OnActionExecuted(ActionExecutedContext filterContext)
    {

    }
}

What this does is check the current HttpContext.ModelState to make sure it is valid. This allows us to ensure we don’t have to make this check in each endpoint. Note, we don’t have to add anything in the OnActionExecuted method since this filter is used exclusively before the action is called.

In the OnActionExecuting call, if the model state is NOT valid, we need to return the 400 – Bad Request response as the result.

Now, let’s register this in our startup. We do this by applying it in the ConfigureServices call and specifically in the options of the AddMvc call:

Startup.cs

public void ConfigureServices(IServiceCollection services)
{
    services.AddMvc(options =>
    {
        options.Filters.Add(typeof(ValidatorActionFilter));
    });
}

And that’s it! So now your endpoints will return the proper response such as:

MyInputModel.cs

public class MyInputModel
{
    [Required]
    public string SomeRequiredValue { get; set; }
    public string SomeNotRequiredValue { get; set; }
}

MyController.cs

public class MyController : Controller
{
    public ActionResult DoSomeAction([FromBody]MyInputModel input)
    {
         return Ok("You did it"!)
    }
}

The request of:

{
    "someNotRequiredValue": "Hey"
}

Will return 400 - Bad Request - "The SomeRequiredValue field is required"

And the request of:

{
    "someRequiredValue": "Yo"
    "someNotRequiredValue": "Hey"
}

Will return 200 - Ok - "You did it!"

So go forth and validate those input models with ease!


If you like what you see, donā€™t forget to follow me on twitter @Suave_Pirate, check out my GitHub, and subscribe to my blog to learn more mobile developer tips and tricks!

Interested in sponsoring developer content? MessageĀ @Suave_PirateĀ on twitter for details.