Session 5 Part 2 Building A Dialog Lab Instructions 181112
Session%205%20Part%202%20-%20Building%20a%20Dialog%20Lab%20instructions%20181112
Session%205%20Part%202%20-%20Building%20a%20Dialog%20Lab%20instructions%20181112
Session%205%20Part%202%20-%20Building%20a%20Dialog%20Lab%20instructions%20181112
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Session 5 part 2: Building a Dialog
IBM Watson Assistant
Lab Instructions
Laurent Vincent
Content
Let’s get started ..................................................................................................................... 3
1. Overview ................................................................................................................... 3
2. Objectives ................................................................................................................. 3
3. Prerequisites ............................................................................................................. 3
4. Scenario .................................................................................................................... 4
5. What to expect when you are done .......................................................................... 5
Gathering information with Slots ......................................................................................... 6
6. Bot Control precreated intents. ................................................................................. 6
7. Add Pizza Ordering node and slots .......................................................................... 7
8. Manage the basic order information : pizza size....................................................... 9
9. Manage the basic order information : pizza type .................................................... 12
10.
Manage the toppings the user would like to add.................................................. 15
11.
Manage the toppings the user would like to remove............................................ 17
12.
Manage the confirmation ..................................................................................... 19
13.
Manage Handlers................................................................................................. 20
14.
Test your Slots ..................................................................................................... 23
Managing nodes and folders .............................................................................................. 27
15.
Add a folder.......................................................................................................... 27
Understanding digressions ................................................................................................ 28
16.
Configure your digressions .................................................................................. 28
17.
Test your digressions ........................................................................................... 32
Serverless Conversation ..................................................................................................... 34
18.
Create a space in US South region ..................................................................... 34
19.
Create a weather service ..................................................................................... 36
20.
Instanciate a IBM Function using Weather service .............................................. 37
21.
Get IBM Function credential ................................................................................ 41
22.
Update the Welcome statement........................................................................... 42
23.
Create the weather branch .................................................................................. 43
24.
Test your serverless conversation ....................................................................... 45
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Let’s get started
1. Overview
The IBM Watson Developer Cloud (WDC) offers a variety of services for developing
cognitive applications. Each Watson service provides a Representational State
Transfer (REST) Application Programming Interface (API) for interacting with the
service. Some services, such as the Speech to Text service, provide additional
interfaces.
The Watson Assistant service combines several cognitive techniques to help you build
and train a bot - defining intents and entities and crafting dialog to simulate
conversation. The system can then be further refined with supplementary technologies
to make the system more human-like or to give it a higher chance of returning the right
answer. Watson Conversation allows you to deploy a range of bots via many channels,
from simple, narrowly focused bots to much more sophisticated, full-blown virtual
agents across mobile devices, messaging platforms like Slack, or even through a
physical robot.
The illustrating screenshots provided in this lab guide could be slightly different from
what you see in the Watson Assistant service interface that you are using. If there are
colour or wording differences, it is because there have been updates to the service
since the lab guide was created.
2. Objectives
Watson Conversation Service provides several options to manage Conditions, and
possibility to have several answers to make your bot more human.
In this lab, you will:
• Learn how to use IBM Cloud Function from the dialog
• Gather information with Slots
3. Prerequisites
Before you start the exercises in this guide, you will need to complete the following
prerequisite tasks:
•
Session 5 part 1 – building a dialog lab Instructions
•
The instructor provided you the link to get labs content. You may download
each file individually.
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Reminder of IBM Cloud URLs per location:
Location
URL
US
https://console.ng.bluemix.net/
UK
https://console.eu-gb.bluemix.net/
Sidney
https://console.au-syd.bluemix.net/
Germany
https://console.eu-de.bluemix.net/
4. Scenario
Use case:
A Hotel Concierge Virtual assistant that is accessed from the guest room
and the hotel lobby.
End-users: Hotel customers
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5. What to expect when you are done
At the end of session, you should get a more complex dialog using several conditions
and answer in the same node.
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Gathering information with Slots
To gather information, you have created a branch, now you can use Slots to do it and
simplify your dialog.
You can think of slots as the chatbot version of a web form in which users must fill out
required fields before they can submit the form. Similarly, slots prevent the flow of
conversation from moving on to a new subject until the required values are provided
You are going to build a chatbot to order pizza. To do it, the chatbot must gather the
size and the type of your pizza. We assume that your hotel can deliver such a service.
6. Bot Control precreated intents.
At the end of the acquisition of all information, we are going to request a validation of
the order. To do this we are using 2 existing intents #Bot_Control_Approve_Response
and #Bot_control_reject_Response.
1. Go back to Content Catalog tab
2. On Bot Control row, Click Add to workspace
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7. Add Pizza Ordering node and slots
The best should be to create a node to manage any orders, we will simplify the lab
and order only pizza which can be delivered in the guestroom.
1. Go back to Dialog page
2. Add a node below Find a restaurant branch or Greeting node
3. Set #order_pizza as condition and Pizza Ordering as name
4. Click Customize
5. Switch Slots on and enable Prompt for everything
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6. Click Apply
7. At the bottom of the edit page click Add slot 2 times
8. Fill the slots like these
Check for
@pizza_size
@pizza_type
@pizza_toppings.values
@pizza_notoppings.values
#Bot_Control_Approve_Response
|| #Bot_Control_Reject_Response
Save it as
$pizza_size
$pizza_type
$pizza_toppings
$pizza_notoppings
$pizza_confirmed
If not present, ask
What size of pizza do you want?
What type of pizza do you want?
I have you for $pizza_size
$pizza_type $texttoppings. Is it
correct?
9. In the filed If no slots are pre-filled, ask this first enter : Can you provide us the
pizza size (small, medium, large) and the pizza type (vegetarian, mexicana, quatro
formaggi, pepperoni, margherita)?
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8. Manage the basic order information : pizza size
1. Click settings/customize/edit slot icon of the pizza_size slot
2. In the configure slot window, click on the 3 dots menu and select enable
conditional responses
3. In Found frame, on the first row, click edit icon
4. Open the context editor. (the 3 dots)
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5. Fill the slot ‘found” like this
Condition : $pizza_size:small && $pizza_type:vegetarian
Context variable : pizza_size
Context value : null
Respond : Sorry, we do not serve small vegetarian pizza. Please select different
type or size.
The simplest response example should be just to confirm the size of the pizza. Here
we illustrate the capability to check the provided value according to some other context
variables.
6. Click back
7. In Not found frame, enter the respond:
condition: true
response: Please provide size of the pizza, e.g small, medium or large.
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Below the first slot:
8. Click Save
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9. Manage the basic order information : pizza type
1. Click settings icon of the pizza_type slot
9. In the configure slot window, click on the 3 dots menu and select enable
conditional responses
10. In Found frame, on the first row, click edit icon
11. Open the context editor. (the 3 dots)
12. Fill the slot ‘found” like this
Condition : $pizza_size:small && $pizza_type:vegetarian
Context variable : pizza_type
Context value : null
Respond : Sorry, we do not serve small vegetarian pizza. Please select different
type or size.
13. Click back
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2. In the Found frame, add 3 more responses and condition like that:
Resp2 condition: event.previous_value and
event.previous_value!=event.current_value
Resp2 response: Ok replacing with
event.current_value ?>.
and without
5. Click Back
6. In Found frame, Click Edit icon for the second condition, then open the Context
editor and fill it like this:
Resp1 context variable: texttoppings
Resp1 context value: with
7. Click Back
8. Click Save
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11. Manage the toppings the user would like to remove
1. Click settings icon of the pizza_notoppings slot
2. In the configure slot window, click on the 3 dots menu and select enable
conditional responses
3. In Found frame, add 2 responses and condition like that:
Resp1 condition: $pizza_notoppings && $pizza_toppings
Resp1 response:
Resp2 condition: $pizza_notoppings
Resp2 response:.
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4. In Found frame, Click Edit icon for the first condition, then open the Context editor
and fill it like this:
Resp1 context variable: texttoppings
Resp1 context value: with and without
5. Click Back
6. In Found frame, Click Edit icon for the second condition, then open the Context
editor and fill it like this:
Resp1 context variable: texttoppings
Resp1 context value: without
7. Click Back
8. Click Save
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12. Manage the confirmation
9. Click settings icon of the pizza_confirmed slot
10. In the configure slot window, click on the 3 dots menu and select enable
conditional responses
11. In Found frame, add 2 responses and condition like that:
Resp1 condition: #Bot_Control_Approve_Response
Resp1 response: Your pizza order will be finished in few minutes. Please feel free
to place another order right now
Resp2 condition: #Bot_Control_Reject_Response
Resp2 response: The order has been cancelled.
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1. In Not found frame, enter the respond:
Resp1 condition: true
Resp1 response: Sorry, I did not understand. Can you please write yes to confirm
the order or no to cancel the order all together? You can also yet change the type
or size. Just say e.g. '"small Margherita.
12. Click Save
13. Manage Handlers
You can optionally define node-level handlers that provide responses to questions
users might ask during the interaction that are tangential to the purpose of the node.
Right now, the handlers enable users to leave the order or get some help.
1. Edit the Pizza_ordering node
2. Click Manage handlers
You are going to add 3 handlers.
3. Click twice Add handler
Note: to open the json editor, you must click on edit icon of the selected row then
the 3 dots on the new window
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4. Fill the 3 handlers as defined below (for second and third open context editor)
Handler1 condition: #General_Agent_Capabilities
Handler1 response: Please provide pizza size and type, e.g large margherita, small
margherita.
Handler2 condition: #reset
Handler2 response: Resetting
Handler2 Context 1 Variable: pizza_size
Handler2 Context 1 Value: null
Handler2 Context 2 Variable: pizza_type
Handler2 Context 2 Value: null
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Handler3 condition: #exit
Handler3 response: exiting
Handler3 Context 1 Variable: pizza_size
Handler3 Context 1 Value: "no_def"
Handler3 Context 2 Variable: pizza_type
Handler3 Context 2 Value: "no_def"
Handler3 Context 3 Variable: pizza_confirmed
Handler3 Context 3 Value: false
Then you should have
5. Click Save
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14. Test your Slots
1. Open Try it out panel and Enter successively:
I want to order a pizza
pepperoni
A small one
Yes
2.
Click Clear
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3. Enter successively
I want to order a small vegetarian
so, a large one
Yes
4.
Click Clear
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5. Enter successively
I want to order a large vegetarian with anchovies and no olive
Yes
6.
Click Clear
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7. Enter successively
I want to order a small pizza
stop my order
If you open the context variable panel, you retrieve the values set by the handler. The
client application has to understand that the command was cancelled. That’s a
possibility to stop the slot.
You can run some other tests and order a pizza by using your chatbot.
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Managing nodes and folders
We can group dialog nodes together by adding them to a folder.
•
•
•
•
It allows a dialog designer to organize content based on topics
It is a much easier dialog tree navigation and understanding
It allows performing of bulk setting of node settings at the folder level instead of
one by one
It is an easier separation of duties for multiple people working on the same bot
Folders have no impact on the order in which nodes are evaluated. But if a condition
is specified, the service first evaluate the folder conditions to determine whether to
process the nodes within it.
The nodes inherit of the digression settings of the folder.
15. Add a folder
The best should be to create a node to manage any orders, we will simplify the lab
and order only pizza which can be delivered in the guestroom.
1. On the dialog tab, Select Greeting node and click Add folder
2. Name it Hotel Amenities Management
We don’t apply neither condition nor settings, as we just want to organise our
dialog.
3. Move Hotel Hours and Hotel Locations nodes in it.
If we make some test the behaviours of the conversation stay the same.
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Understanding digressions
Digressions allow for the user to break away from a dialog branch in order to
temporarily change the topic before returning to the original dialog flow. In this step,
you will start to order a pizza, then digress away to ask for the restaurant's hours. After
providing the opening hours information, the service will return back to the pizza
ordering dialog flow.
16. Configure your digressions
We are going to configure 2 nodes and 1 folder.
4. Select Pizza Ordering node
5. Click on Customize button then go to the Digression tab
This is the default settings :
Digression cannot go away from this node
Digression can come into this node
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We want to enable to go away from this node and come into this node
6. Enable this option go away from this node, don’t update the second option
7. Click Apply
8. Select Hotel Amenities management folder
9. Click on Customize button
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10. Select the option Return after digression
11. Click Apply
The settings will be applied to all nodes into the folder : Hotel Locations and Hotel
Hours
Now, you are going to create talk to concierge node which requires to not return
after digression.
12. Select talk to concierge node.
13. Click Customize, then go to Digressions tab
We keep the default digression behaviour as we want to be able to come into this
node without return after digression
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14. Select Intents Confidence rate node.
15. Click Customize, then go to Digressions tab
As this is a technical node, we don’t want any digression from or to this node. We
switch off the option come into this
16. Turn off the second option
17. Click Apply
18. Repeat the previous steps to disable the digression for the Anything else node.
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17. Test your digressions
1. Open the Try is out panel and click Clear
2. Enter : I want to order a pizza
3. Click on the location Icon (right to the answer)
The Pizza Ordering node is highlighted, which was expected/
4. Enter : When the hotel restaurant is open?
The bot digresses away from the Pizza Ordering node to process the Hotel
Hours node. The service then returns to the Pizza Ordering node, and prompts
you again for the size of pizza.
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5. Enter : ok, I stop my order to conclude the ordering
6. Enter : where is it? to illustrate that the service kept the context.
7. Click Clear
8. Enter successively :
I want to order a pizza
finally, I want to talk to concierge
The bot digresses away from the Pizza Ordering node to process the Talk to
concierge node and not returns to the Pizza Ordering node.
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Serverless Conversation
The objective of the section is to define actions that can make programmatic calls to
external applications or services and get back a result as part of the processing that
occurs within a dialog turn.
You can use an external service to validate information that you collected from the
user or perform calculations or string manipulations on the input which are too complex
to be handled by using supported SpEL expressions and methods. Or you can interact
with an external web service to get information, such as an air traffic service to check
on a flight's expected arrival time or a weather service to get a forecast. You can even
interact with an external application, such as a restaurant reservation site, to complete
a simple transaction on the user's behalf.
By today, you will add a new to get information about the weather forecast in Nice. We
limited the location in Nice for our lab, but you can get such an information for any city
around the world.
18. Create a space in US South region
The weather service is cloud foundry service which require a space in a targeget
region. To keep consistency, you have to create a space in US South / Dallas before
being able to create a weather service in US South / Dallas.
If you have already a space in US South, select this one and move to the section
‘Create a weather service’, if not continue.
1. Select menu Manage / Account / Cloud Foundry Orgs
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2. Click Actions of your organisation then Spaces
3. Click Add a Space
4. Select US South / Dallas, and give a name to your new space.
5. Click Save
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19. Create a weather service
1. Go back to your Dashboard
2. Click Create resource
3. Look for weather service
4. Click Weather Company Data tile.
5. Determine a name for your service and Select the region / location used for your
Watson Assistant service. It should be US South or Dallas.
6. Click Create
7. Go to the Service credentials page
8. Create a new credential and copy username and password and host
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20. Instanciate a IBM Function using Weather service
You are going to create the Function called by Watson Conversation. This Function
will call the Weather company service.
1. Click on Hamburger menu
2. Click Functions
3. Click Start Creating
4. Be sure to select the region used by your Watson Assistant
5. Click Create Action
6. Enter Weather as name, keep Node.js 6, click Create
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7. Copy / paste the code below into the Code frame of the Weather IBM Cloud
Function. (the code can also be copied from the file Weather_Cloud_Function.js)
// Licensed to the Apache Software Foundation (ASF) under one or more contributor
// license agreements; and to You under the Apache License, Version 2.0.
var request = require('request');
/**
* Get hourly weather forecast for a lat/long from the Weather API service.
*
* Must specify one of zipCode or latitude/longitude.
*
* @param username The Weather service API account username.
* @param username The Weather service API account password.
* @param latitude Latitude of coordinate to get forecast.
* @param longitude Longitude of coordinate to get forecast.
* @param zipCode ZIP code of desired forecast.
* @return The hourly forecast for the lat/long.
*/
function main(params) {
console.log('input params:', params);
var username = params.username || '';
var password = params.password || '';
var lat = params.latitude || '43.659';
var lon = params.longitude || '7.192';
var language = params.language || 'en-US';
var units = params.units || 'm';
var timePeriod = params.timePeriod || '10day';
var host = params.host || '';
var url = 'https://' + host + '/api/weather/v1/geocode/' + lat + '/' + lon;
var qs = {language: language, units: units};
switch(timePeriod) {
case '48hour':
url += '/forecast/hourly/48hour.json';
break;
case 'current':
url += '/observations.json';
break;
case 'timeseries':
url += '/observations/timeseries.json';
qs.hours = '23';
break;
case '3day':
url += '/forecast/daily/3day.json';
qs.hours = '23';
break;
default:
url += '/forecast/daily/10day.json';
break;
}
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console.log('url:', url);
var promise = new Promise(function(resolve, reject) {
request({
url: url,
qs: qs,
auth: {username: username, password: password},
timeout: 30000
}, function (error, response, body) {
if (!error && response.statusCode === 200) {
var j = JSON.parse(body);
console.log('body:', body);
console.log('j:', j.forecasts[0].narrative);
var tmp = { narrative: j.forecasts[0].narrative};
resolve(tmp);
// resolve(j);
} else {
console.log('error getting forecast');
console.log('http status code:', (response || {}).statusCode);
console.log('error:', error);
console.log('body:', body);
reject({
error: error,
response: response,
body: body
});
}
});
});
return promise;
}
8. Replace , and in the source code with the
username, password and host of your weather service instance.
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9. To test your action, click Invoke
The action should return the current weather in Nice. You can imagine to provide the
Hotel location to get the local weather forecast.
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21. Get IBM Function credential
1. On top of the page click Actions /
2. Expand Getting Started menu and click API Key
3. Click copy icon
4. Paste it in any text editor
You should get something like this:
:
The User ID is all characters before :
The Password is all characters after :
5. Copy also the Current Namespace
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22. Update the Welcome statement
You are going to add in the Welcome statement information about the weather
forecast.
1. Go back to Watson Assistant user interface
2. Select and expand Start conversation node
3. Select welcome node to edit it
4. Open the Json editor and update in the Context variable : private.mycredential
The
Context value
: {"user":"","password":""}
User and password must be replaced with your IBM Cloud Functions credentials
In the real implementation the credential must be manage by the Client Application
which orchestrate the conversation. So this node is useless in this case.
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23. Create the weather branch
We are going to create nodes to leverage the Weather forecast provided by the
weather company service via IBM Cloud Function.
1. Select the anything else node and Add a node above and fill it this
Name : Call Weather Function
Condition : #weather
2. Open the JSON editor
3. Copy Paste the code below
{
"output": {},
"actions": [
{
"name": "//Weather",
"type": "cloud_function",
"parameters": {
"latitude": "$private.location.latitude",
"longitude": "$private.location.longitude",
"timePeriod": "10day"
},
"credentials": "$private.mycredential",
"result_variable": "context.weather"
}
]
}
4. Replace the name space with yours
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5. Add a child to Call Weather Function node and fill it like this:
Name: Display weather forecast
condition: true
response: The forecast today in Nice is $weather.narrative
6. Return to Call Weather Function node and select the option Skip user input
7. Close the node editor
8. Return to Call Weather Function node and select the option Skip user input
9. Close the node editor
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The final Weather branch should look like this:
24. Test your serverless conversation
1. Open try it out frame
2. Enter What is the weather like?
The service should display the weather forecast:
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The final dialog should look like this:
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