- Watson Assistant is an IBM cloud service that uses natural language processing and structured processing to build dialog workflows for chatbots. - It handles intents, entities, context variables, and dialog flow to understand user input and determine responses. - Developers use the browser-based tool to build, test, and train chatbot conversations by managing intents, entities, dialog nodes, and context.

1. 2018
Do You Wanna Build A Chatbot?
Paul Withers – Intec Systems Ltd

2. Paul Withers
ICS Developer, Intec Systems Ltd
OpenNTF Board Member
IBM Lifetime Champion
Notes Developer XPages Developer Java Developer
Microservices
Developer

3. Statbot
• Most chatbot conversations start with “Hi”
• There are more than 30,000 Facebook chatbots
• 63% would message online chatbot to speak to business / brand
• 37% of Americans willing to purchase through a chatbot
• 73% of Americans wouldn’t use a company’s chatbot after a bad
experience
• 75% want to know if they’re talking to a chatbot or a person
https://chatbotsmagazine.com/11-amazing-facts-you-might-not-know-about-chatbots-7764213406e0

4. What is Watson Assistant?
Watson Assistant (formerly Watson Conversation) is an IBM chat cloud
service that uses natural language processing and more structured
processing to build a dialog workflow that an external system can interact
with and tracking the session for a specific conversation, fronted by a
browser-based interface for development, testing and DevOps.

5. IBM Chat Cloud Service
• Other providers may have similar services for managing a chatbot’s flow
• Cloud only currently. Product Manager has confirmed on prem is coming
• Training doesn’t retain personal data, conversations available for a period of
time for review and training
• You can’t improve the results if you don’t know what was asked
• Obviously includes a database for storing intents, entities etc
• But no setup or management required
• Intents, entities and dialog flow can be exported / imported as JSON
• Programmatic APIs to add / modify intents etc

6. Natural Language Processing
INTENTS
• Phrases and variants
• Case sensitivity is irrelevant
• Grammar is irrelevant
• Will cope with some mis-typing
• Can include entities
• Training algorithms will extrapolate

7. More Structured Processing
ENTITIES
• Key values
• Can include fuzzy matching
• Synonyms (database, Notes, Domino, NSF)
• Regex pattern matching (e.g. email format)
• Also some system entities available, e.g. @sys-person
• System entities cannot be trained

8. Dialog Flow
• “Welcome” and “Anything Else” nodes
• Workflow rules to respond to intents or entities or static conditions
• Flow can define how to deal with follow-on responses
• Flow can define how to handle digressions
• Steps can set / modify state
• Steps return a JSON object containing text, images, buttons etc and
additional JSON data

9. External Systems
• Watson Assistant is not a User Interface
• Does not typically interact directly with external databases
• But can be directly connected to cloud functions
• Does not manage authentication

10. Tracking the Session
• Done via context JSON object
• Passed into and out of every interaction with Watson Assistant
• Can be updated by dialog flow
• E.g. depending on entities recognised
• Can be updated by external systems

11. Browser-based Tool
• Accessible from your IBM Cloud dashboard
• Managing workspaces for a conversation
• Developer panel for the intents, entities and dialog
• APIs are available for programmatically managing
• Basic testing client in sidebar allows testing / training
• DevOps area for reviewing usage and updating training

12. This is Watson Assistant

13. What Is A Chatbot?
A chatbot comprises a user interface where a person provides dialog
content which is passed with any metadata on through one or more
services and receives a fixed-format response for the application to return
for the person.

14. User Interface
• User interface will vary, including e.g.
• Custom interface (e.g. XPage, Java UI, JavaScript UI)
• Out-of-the-box configurable plugin (e.g. WordPress)
• Hooked service (e.g. Watson Workspace app + “Make it Cognitive”)
• Speech interface (e.g. Alexa, IBM Voice Gateway)

15. Dialog Content with Metadata
• Remember context JSON object

16. One or More Services
• Watson Assistant may be just one of the services
• Out-of-the-box plugins / hooked services will be limited
• How do you integrate with external systems / additional services?
• How many calls will it make, how to avoid abuse?
• Custom application (“orchestration layer”) provides greater flexibility,
control and ability to hook in whatever you want
• E.g. SSJS, XPages Java, LotusScript, Node-RED, JavaScript

17. Don’t Understate Architecture!
• Create an investment management chatbot

18. Fixed-format Response
• Watson Assistant returns JSON object
• UI layer handles manipulation of e.g. JSON object to user-friendly
output

19. This Is A Chatbot

20. This Is What I’m Covering

21. Not This

22. Workspaces
• Not the Notes Workspace
• Not a Watson Workspace
• One of a collection of chat “spaces” – 5 for Lite, 20 for Standard
• Avoids conflicts or misunderstanding in larger “chats”
• Has its own id, URL, username and password
• Your app / plugin will need these to interact with the
correct workspace

23. Intents
• Groupings for particular requests
• “buy a ticket”, “get directions”, “hotel”
• 100 for Lite, 2000 for Standard
• Examples add alternative phrasing, 25000 max
• Can be imported or exported as CSV
• Can be managed programmatically

24. Intents
• Phrasing may include entities
• Either explicit values or @entity-name – read Documentation!
• Intent example + entity variants + a bit of magic = training variants

25. Entities
• Groupings for specific nomenclature
• 25 for Lite, 1000 for Standard – system entities count too
• An entity can include different entity values
• Entity “@beer” includes “beer”, “beer_types” and “wine”
• Will identify entity as e.g. @beer:beer, @beer:wine
• Values can use synonyms up to 64 characters, entity can be enabled
for fuzzy matching
• 100,000 entity values and 100,000 synonyms max

26. Entities
• Values can alternative use pattern matching
• Up to 5 patterns per entity value
• Enter as regex, max 512 characters
• Synonyms or Patterns – not both!

27. System Entities
• Can be enabled for more generic recognitions
• E.g. @sys-date, @sys-number, @sys-person
• Cannot be trained
• What you get is what you get!
• Tend to be US-focused
• Dates are always MM/dd/yyyy, can’t be defined as dd/MM/yyyy

28. Dialog
• Dialog is a tree-based flow of nodes
• 25,000 for Lite, 100,000 for Standard
• “If” statements based on recognitions of intents, entities, context etc
• Customise for:
• Gathering multiple pieces of information in one go (slots)
• Define multiple responses based on sub-conditions
• Allow digressions into / out of this node

29. Dialog
• Steps
• Define responses, if required
• Text, Option, Pause, Image
• But does your UI handle all these response types?
• Set or modifies (optionally) context variables
• Define what to do next

30. Context
• Dialog is stateless
• Context is JSON object of context variables for each conversation
• Use “context editor” to manage
• Expression language methods available, e.g. <? input.text ?>, <?
@email:literal ?> etc
• Variables can be complex JSON objects and arrays
• Can be passed in and is passed out of nodes
• If it can be passed in, it can be modified externally!

31. External Integration
• How do you interact with third-party services from Watson Assistant?
• Code it in your orchestration layer, update context object, pass result back
• Make programmatic call from Watson Assistant node
• Client: defines programmatic name your chatbot client understands and
can interpret to return result to dialog
• E.g. JS function if chatbot is a web page
• Not all chatbot clients may support client calls
• Server: IBM Cloud Function, see video
• Runs after context variables have been updated

32. Cloud Functions
• Repository for “Serverless” REST APIs
• Currently beta, only in US South and Germany regions
• Cloud Functions instance uses same location as Watson Assistant instance
• Can’t call Cloud Functions web action
• Single call must complete in 5 seconds, multiple calls must complete within 7
seconds
• Up to 5 calls from a node, processed in parallel
• To call in series, use Cloud Functions sequence or multiple nodes

33. Testing
• Use “Try it out” panel
• “Manage Context” allows view / edit
of context variables
• Change intent recognised or mark
irrelevant
• Turn on “node tracing” to highlight
visited nodes for this response

34. Debugging Dialog
• Adam Newbold’s Dialog Debugger PHP app
• PHP app can be run
on IBM Cloud
• One of three utilities
also on GitHub including
utility to rename entities

35. Training
• Are you / your business analysts omniscient?
• No? You need to regularly review and train
• Look to update where there is weak understanding
• Review conversations for:
• Incorrect understanding
• Incorrect flows – reorder nodes
• Requirements for additional flows
• Requirements for digression handling

36. Best Practice
• Challenge your users:
• Is a chatbot the right approach?
• Is the chatbot in the right point of access?
• What value is the chatbot adding?
• What is the right tone for the chatbot?
• Design on paper, document on cloud
• Group nodes into folders for easier manageability

37. Best Practice
• More nodes = more complexity = greater risk of ambiguity
• This is why you may want multiple workspaces
• Disambiguation is only available to Premium users
• May need to handle multiple intents, see Compound Questions blog
• May need to handle multiple entities
• Use contextual entities – add annotations to intents
• RTFM to understand how to best code conditions

38. Resources
• Watson Assistant Documentation
• Watson Assistant Demo
• Watson Assistant GitHub Samples
• Watson Assistant YouTube channel
• SDKs for Node, Java and XPages (needs Java 8 and Java security changes)
• Watson Node-Red Nodes (automatically added when creating Node-RED starter on IBM
Cloud)
• Watson Developer Cloud Node-Red Labs
• Chatbots and Watson Assistant blog post
• Simon O’Doherty blog
• Watson Developer Community inviter on Slack

39. Thanks to our 2018 Sponsors

40. Questions?
Paul Withers
Intec Systems Ltd & OpenNTF
pwithers@intec.co.uk
@paulswithers
https://www.intec.co.uk/blog
https://paulswithers.github.io