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! 😀
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:
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:
And now let’s ask for it!
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, don’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? Message @Suave_Pirate on twitter for details.
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/
Alex Dunn 