Project: Build an Alexa History Skill

It's time to create your own Alexa Skill!. In this project, you’ll build a fully functional skill for Amazon’s Alexa that provides year-dated facts from AI History (or some other History of your choosing). Through the process, you’ll get to use the Alexa Skills Kit (ASK) - a current state of the art API for building voice systems.

Getting Started

Set up your Amazon accounts

We highly recommend you complete the Space Geek lab, which steps you through setting up your Amazon Developer and Amazon AWS accounts as well as the project this one is based on. If you’ve already done that you can skip this step. find more detail on this in the classroom or see Amazon's sample fact skill repo

Starter Code

Install your starter code locally.

• Download or clone the starter code from GitHub
• Save it in a directory named AIND-VUI-Alexa. It contains the following directories and files:
  • speechAssets/IntentSchema.json - intents definition for the interactive model
  • speechAssets/SampleUtterances_en_US.txt - utterances for the interactive model
  • src/index.js - skill logic and handlers to be run in AWS Lamda
  • src/facts.js - a list of facts that the skill will use in responses
  • tests/*.js - various unit tests to be run locally with mocha; you do not need to change these

Environment

1. Install Node.js per instructions on the website for your machine.

2. Install dependencies for the project

• Navigate to the AIND-VUI-Alexa/src directory of the starter code and open a terminal window.
• The AWS Lambda function we used in the Space Geek lab was built on Node.js and included a linked alexa-sdk library for Alexa. We will install this locally as well. Install the alexa-sdk library with the following command:

$ npm install --save alexa-sdk

• There should now be a directory named node_modules within the src directory. This is how Node.js attaches libraries for your code.

3. Install development dependencies for unit testing

• We will be using the mocha test framework for Node.js along with chai and aws-lambda-mock-context for local unit testing. Install these with the following command:

$ npm install --save-dev mocha chai aws-lambda-mock-context

• You can run the provided unit tests from the command line within the src directory with the following command. Try it now:

$ npm test

The starter code should pass the starter code tests but fail the rest. As you complete the tasks ahead, you can use these unit tests (and write more yourself) to quickly test code changes prior to deployment to AWS Lambda.

4. JavaScript coding

• At this point, you can open your project with a code editor of your choice. Some free ones that support JavaScript and Node.js include Atom and Visual Studio Code
• Udacity has a free Intro to JavaScript course available that will quickly bring you up to speed on JS syntax. The JavaScript skills needed for this project primarily require following patterns you find in the existing starter code and the use of general coding constructs such as for loops, conditional statements, and arrays. Here are a few links that might be helpful for quick reference:
  • js for loops
  • js conditional statements
  • js arrays
  • js String includes() method
  • js JSON
  • Alexa skill examples
  • Node.js API reference

5. Run the Starter Code on AWS Lambda

• Deploy the starter code to verify that it works with your accounts in its simple form. This is the same process you went through with the Space Geek Lab. If you need a refresher, step-by-step instructions are provided in the classroom.

Tasks

The starter code provides a simple fact skill similar to Space Geek, except a few AI history facts have been provided in an external file, facts.js, instead of the space facts. Each fact includes a 4-digit year in its text, which we will use in the project for a new feature.

The project consists of three parts:

1. Customize the fact skill
2. Add a feature using an additional intent and a slot
3. Add conversational elements

Part 1: Customize the fact skill

1. Choose a history category you wish to use for your skill. You can continue to use the AI History Facts already started for you if you wish.

2. Expand the utterances in the speechAssets/SampleUtterances_en_US.txt file to include at least 15 appropriate utterances for GetNewFactIntent. Examples can be found here.

3. Expand the facts list in facts.js such that there are at least 10 distinct facts, where each includes a 4-digit year in its text. These will be spoken by the Amazon Text-To-Speech algorithm (TTS), so keep in mind where you wish pauses to occur. To hear how it sounds, enter your sentence in the developer portal under the “Test” section:

4. Test it. All “Starter Code” and “Part 1” local unit tests should pass. Try the skill out by uploading your changes to the Interactive model in the Developer Portal and AWS Lambda.

Part 2: Add a feature

In addition to the GetNewFactIntent intent already included, add an intent including a built-in slot named FACT_YEAR that will provide the user with a fact matching the year requested. Name this intent GetNewYearFactIntent. Built-in Amazon slots can be used for the year. Consider using AMAZON.FOUR_DIGIT_NUMBER for this purpose. This is not required, however, if you prefer to try a different slot definition.

1. Provide at least 15 utterances for the new intent.

2. Complete the TODO’s in index.js to implement a handler for GetNewYearFactIntent that will provide a fact for the specific year requested, and provide a random fact if the year is not found in the fact list.

3. Test it. All “Starter Code”, “Part 1”, and “Part 2” local unit tests should pass. Try the skill out by uploading your changes to the Interactive model in the Developer Portal and AWS Lambda.

Part 3: Add conversational elements

As discussed in the videos, conversations with a VUI will seem more natural if the session window can be left open to continue request/response interactions. Additionally, adding randomization to the Alexa response text provides a more natural feel to the conversation.

1. Change your “tell” statements to “ask” statements as directed by the TODO’s in index.js and include reprompt messages as necessary.

2. Change the GET_FACT_MESSAGE snippet to an array of at least 5 similar phrases. Randomize this portion of the Alexa response.

3. Test it. All local tests should now pass. Deploy your changes to the Interactive model in the Developer Portal and AWS Lambda.

4. Provide a screen capture (PNG) from the Service Simulator in the Developer portal of your skill working. The screen capture should include the part of the Lambda Request that shows the GetNewYearFactIntent and slot with the year requested. The Lambda Response side only needs to show that a fact was provided. Note that in order to request a slot with the Simulator, you will need to phonetically request a year. For example, if the year is 2012, the input will need to be “two thousand and twelve” rather than “2012” in the simulator. Save the screen capture for submission with the name skill_simulator.png

Optional Additional Testing

In addition to testing with unit tests and the Service Simulator, you may find it useful to try your skill with one or more of the following:

• Echosim.io: web browser simulator
• Alexa app: view card
• Amazon Echo, Echo Dot, Echo Tap devices: if the device is on the same account as the development code, you can "open" the skill there.

Submission

ADD PROJECT ASSISTANT INSTRUCTIONS

Submit your work by uploading a .zip file containing the following directories and files:

• speechAssets (directory)
  • IntentSchema.json
  • SampleUtterances_en_US.txt
• src (directory)
  • Index.js
  • Facts.js
• skill_simulator.png

You can view the [BETA] rubric here