amz-ai-building-better-bots

Code samples related to Building Better Bots published on the AI Blog

CoffeeBot

CoffeeBot is a transactional chat bot that can help one order a mocha (relies on AWS Mobile Hub and Android).

Consider this conversation:

User: May I have a mocha?

CoffeeBot: What size? small, medium, large?

User: small

CoffeeBot: Would you like that iced or hot?

User: hot

CoffeeBot: You'd like me to order a small mocha. Is that right?

User: Make it a large mocha

CoffeeBot: You'd like me to order a large mocha. Is that right?

User: yeah

CoffeeBot: Great! Your mocha will be available for pickup soon. Thanks for using CoffeeBot!

Let's build this voice bot, an Android App that talks to you using Amazon Polly and Amazon Lex. You'll need the following in addition to your AWS account:

• Android development environment (download)
• To test voice (you can use the simulator for text)
  • An Android device
  • A USB cable for USB debugging (more info for Amazon Fire tablets)

First, we'll create the Lex bot. Then, we'll add some Lambda Functions to bring it to life. Finally, we'll put it all together with Mobile Hub and the Lex Android SDK.

Lex bot

1. Create bot

1. From the Amazon Lex console, create a Custom bot with these settings (you can see these in the "Settings" tab later)
   • Bot name: CoffeeBot
   • Output voice: Salli
   • Session timeout: 10 min
   • IAM role: (accept the default) AWSServiceRoleForLexBots
2. Review the Error handling settings
   • Prompts: (one prompt) Sorry, can you please repeat that?
   • Maximum number of retries: 3
   • Hang-up phrase: (one phrase) Sorry, I could not understand. Goodbye.

2. Create Slot types

Add the following Slot types (each value should be a separate entry) so they show up on the left (remember to "Save slot type" as you go along).

Slot type name	Description	Values
cafeBeverageType	Slot types are shared at the account level so text would help other developers determine if they can reuse this Slot type.	coffee; cappuccino; latte; mocha; chai; espresso; smoothie
cafeBeverageSize		kids; small; medium; large; extra large; six ounce; eight ounce; twelve ounce; sixteen ounce; twenty ounce
cafeCreamerType		two percent; skim milk; soy; almond; whole; skim; half and half
cafeBeverageTemp		kids; hot; iced
cafeBeverageStrength		single; double; triple; quad; quadruple
cafeBeverageExtras		half sweet; semi sweet

3. Create Order Beverage Intent

From the left, add a new Intent called cafeOrderBeverageIntent with the following settings and click "Save" to save the Intent

1. Options (leave options unchecked)
2. Fulfillment: choose "Return parameters to client" for now
3. Confirmation prompt: You'd like me to order a {BeverageSize} {BeverageType}. Is that right? to confirm and Okay. Nothing to order this time. See you next time! to cancel.
4. Sample Utterances: add these to the list of sample utterances so the bot recognizes similar phrases

I would like a {BeverageSize} {BeverageType}
Can I get a {BeverageType}
May I have a {BeverageSize} {Creamer} {BeverageStrength} {BeverageType}
Can I get a {BeverageSize} {BeverageTemp} {Creamer} {BeverageStrength} {BeverageType}
Let me get a {BeverageSize} {Creamer} {BeverageType}

5. Slots

Required	Name	Slot type	Prompt
Yes	BeverageType	cafeBeverageType	What kind of beverage would you like? For example, mocha, chai, etc.
Yes	BeverageSize	cafeBeverageSize	What size? small, medium, large?
	Creamer	cafeCreamerType	What kind of milk or creamer?
	BeverageTemp	cafeBeverageTemp	Would you like that iced or hot?
	BeverageStrength	cafeBeverageStrength	Single or double?
	BeverageExtras	cafeBeverageExtras	extras?

4. Test

Build the app and test some of the Utterances in the Test Bot dialog at the bottom right of the Lex Console. For example, if you say May I have a chai?, does Lex correctly map chai to the BeverageType slot?

Lambda Function

1. Create the cafeOrderCoffee function by saving cafeOrderCoffee_lambda.js as a Node.js 4.3 function
   • You can get the function source here
   • (No need to set up a trigger; you can accept default values for most of the configuration)
   • Choose an IAM role that includes the AWSLambdaBasicExecutionRole Managed Policy. If no such role exists, you can create a new IAM Role using one of these approaches:
     • Choose "Create new role from template(s)", provide a role name, and choose Basic Lambda permissions from the "Policy templates" dropdown
     • Choose "Create a Custom role", which should open up a new tab where an IAM role is shown; review the policy document and click "Allow"
2. Configure the Test event and test to confirm the function works as expected (see cafeOrderCoffee_test.json)
   • you can get the event source here
3. You'll notice that the function checks the bot name it receives (if (event.bot.name !== 'CoffeeBot')); remember to change this value in the function and in the test event to match the name you used for your bot

Test the bot

1. From the Lex Console, select the CoffeeBot bot and choose Latest from the version drop down to make changes
2. Modify the cafeOrderBeverageIntent Intent to associate it with the new cafeOrderCoffee Lambda function (select "AWS Lambda function" in the "Fulfillment" area); remember to click "Save"
   • The Lambda function overrides the "Goodbye message" (so we don't configure it here)
3. Build the app
4. Test using Lex Console; do you see any responses when you ask May I have a mocha?

Android App

1. From the Mobile Hub console, create a new project called CoffeeBot.
2. Add the "Conversational Bots" feature to the project. When prompted, import CoffeeBot. Mobile Hub takes care of a number of important details behind the scenes. A new Amazon Cognito Federated Identity Pool is created for this new app along with roles so that the users can interact with Lex (using voice and text).
3. Source code for the new app is immediately available for download.
4. Follow the instructions in the READ_ME/index.html file to setup, compile, and run the app.