Note: This article was updated on April 29, to add links for a Leigh Williamson blog post that I found informative.
In an earlier blog post, IBM Watson Fueled Chatbots — For Our Health, I talk about the costs and benefits of a chatbot, and show you how a chatbot is within the reach of most organizations. I then branch out and discuss how to make updates to your chatbot, in my post called, I Love My Watson Chatbot — How Do I Update It? Now we look at doing some continuous testing, and how to do some basic version control and deploy new chatbot updates.
How Are We Doing?
In order to see how well your chatbot is performing, you need to put some automated testing of your chatbot in place. This will help you get some objective measures of chatbot accuracy and performance, and will also give you some insight into areas where your chatbot could stand to improve. Keep in mind that testing cognitive applications is fundamentally different from testing more traditional applications. Traditional applications relied on path testing, and code coverage. Due to the non-deterministic nature of cognitive applications, we end up having to use statistical models for our testing.
I STRONGLY suggest using the Python notebooks listed below. These can be run on your own machine, or from within Watson Studio. They are a great starting point for your automated analysis of your chatbot. Over time you can improve these notebooks and expand on some of the testing techniques in them.
Log Analysis Workbooks — these two notebooks focus on slightly different areas, and do an analysis of your chatbot based on your logs.
My K-Fold Notebook — not as full-featured as the two notebooks above, but I like mine because it’s simple. For a new Python user, it also has a straightforward approach to k-fold testing that is easier to understand.
Getting the feeling that you need to know a little bit of Python? So am I. You don’t have to be a Python expert to do any of this, but you should at least understand the basics of Python, and how to use and manipulate Python notebooks. Don’t get intimidated — you don’t need to know EVERYTHING before you start — just jump in and learn things as you go.
Making Changes Without Impacting Production
Some change management scenarios are enabled by using Watson Assistant’s new versioning capability. This versioning is a bit different than some versioning that you might be used to — it’s not like a typical software development check out, check in paradigm. Instead, it is more like a baseline establishment paradigm, where a version (or baseline) is created as a snapshot of the model at some point in time. The current state of the model (which you see in the UI) is always the tip, or development version.
So if you go into your Watson Assistant Plus instance, and go into your skill, you can just click on “Versions” in the left-hand nav bar, and you will see the versions screen.
Versions Screen in Watson Assistant
In order to drop a new version (or baseline), just click on the “Save a new version” link in the upper right corner. You should then fill in something useful (think about naming conventions!) for the description, before pressing “Save”. We will talk more about naming conventions below, when we start to talk about some typical change management workflows.
Typical Change Management Workflows
A typical change management workflow will allow you to easily scope, contain, and then deploy changes to your chatbot. What I am proposing here is something that might not satisfy a “DevOps Purist”, since we have production and dev/test resources residing in the same service.
Keep in mind that this approach CAN easily be modified so that instead of moving the versions around within a Watson Assistant instance, we could export the model from one Watson Assistant environment (say the test environment), and then import it into another environment (say the production environment). These environments could be located in different resource groups, with individualized access and permissions tailored for each environment/resource group.
So without further explanation, here is a high-level view of a typical lightweight change management process for your Watson Covid-19 chatbot:
Get suggested changes for the next cycle. Save these in some agreed upon format/tool with changes for your Covid-19 bot.
Agree on changes and scope of changes for the next cycle. For those of you who are familiar with Agile development principles, think of this as sprint planning….
Apply these changes in the tip (development) version of your Covid-19 bot
Test development Covid-19 bot version — validate your changes — make final revisions
Run automated quality tests against development bot version (yes, I wasn’t kidding about automated tests earlier, do this now — and you will thank me later)
Create a new version in the Covid-19 bot — call it “<YYYY-MM-DD> Production”
Get formal sign off for promotion of new version to production.
Move the Assistant pointer for the production Covid-19 bot to the new version (“<YY-MM-DD> Production”)
Backup the Covid-19 bot, with an export of the dialog skill to JSON — store the JSON file with name “<YYYY-MM-DD>_Production.json”
Move the Assistant pointer for the development Covid-19 bot to the tip version
Go back to step 1 and do it again……
Note that I have been non-specific about tickets/issues/tools to be using. I just trust that you are using some sort of change control — no matter how simple. You want to get your stakeholders in the habit of making change requests and then APPROVING those requests — just so we avoid any “Why did you do that?” situations. You can implement this on the IBM Cloud (a free version of GitHub hosted on the IBM Cloud), where you can do real lightweight change management, or you can use your existing change management tooling and infrastructure. It’s completely up to you.
Change Management for the Advanced User
The simple change management process outlined above will work for simple deployments, and is probably robust enough for a chatbot that is not expected to be deployed on a long term basis. What about change management for more established chatbots? Or for chatbots that are part of a larger system that is currently under some more formal type of change management? What can we do in those cases?
In those cases, and for users wishing for a more robust and formal change management or DevOps infrastructure, you will want to read A comparison of DevOps choices for IBM Watson Assistant byLeigh Williamson. Leigh is a peer of mine who has spent some time thinking about this, and more importantly, he has spent some time actually implementing these types of change management systems.
What’s Next?
So if you have been following this series of blog posts, you have deployed a longtail chatbot that answers Covid-19 questions. You have learned how to update that chatbot, and how to test and version control your changes. What’s left to do? At this point you know enough to be dangerous — it might be time to fill in some of those learning gaps. My blog posts tell you just what you need to know, but there are so many other Watson techniques and capabilities that you can use to make your end-user experience even better. Maybe you should check out some of the Watson Covid-19 specific learning resources that are out there.
In my earlier blog post, IBM Watson Fueled Chatbots — For Our Health, I talked about the costs and benefits of a chatbot, and showed you how a chatbot is within the reach of most organizations. I also walked you through a step-by-step guide of creating that initial chatbot. It’s not that hard to do, and the costs are minimal (or even zero if you take advantage of the IBM “Citizen Chatbot” campaign). Once you get that initial chatbot deployed, you have to update it. Given how fluid the current situation is in many locations, in terms of the guidance and response to the Covid-19 pandemic, you will be changing things OFTEN. So how do we do that? How do we maintain our chatbot?
In the following sections, I will briefly touch on how to do some basic things in Watson Assistant. You will want to find more complete guidance on all sorts of topics on our Watson Assistant landing pages, or even in the product documentation. I’ll try to make this as generic as possible since everyone will have slightly different implementations of things.
Getting Comfortable with Your Watson Chatbot
A good way to become familiar with your Watson Assistant implementation is to run some k-fold testing against it, and to look at and understand the results. The Python notebooks used for these can help you begin to understand what your chatbot is doing, how it is doing it, and gives you a foundation of test results to build on. I’ll cover this entire topic in a different post.
If you’re uncomfortable with this, you can just open up your Watson Assistant instance, and navigate into your Dialog skill. Once in there, go and look at your intents. Get familiar with the intents that you are looking for. You can export the training data for your intents. Just click on the “Export Intents” icon, and specify a CSV file to export to.
Exporting intents and training data to CSV file
Once that file has been exported, you can open the CSV file and see all of the training data and intents. Use this same approach to export your entities and their training data, so you can familiarize yourself with those as well.
I will often keep a list of intents and their general topics on a single sheet of paper, along with entities and their focus, on my desk. It will help me keep things straight when I go in and begin to make changes to the existing dialog nodes. The typical list might look something like this:
#AnimalContact — Human and animal interaction and guidelines #AttendingFuneral — info on funerals and attendance … @phone — US phone number pattern xxx-xxx-xxxx @school_type — high school, college, etc
Simple Changes to Specific Responses
Making simple changes to the “canned” responses that our chatbot has is pretty easy. It’s also one of the best ways to begin learning and getting comfortable with the Watson Assistant.
So what can we do? Let’s start at the beginning (always a great place to start!), and jump into our Watson Assistant dialog skill. Once in, click on the “Dialog” menu option, to display the dialog tree. Remember that the dialog tree nodes are evaluated for each user interaction, and they get evaluated starting from the top, and continuing to the bottom of the tree.
Take a look at the top node in your dialog tree. You can see that it has a name, followed by a section asking if the assistant recognizes some event. This is evaluating the user’s input, and looking at the intent and possibly the entities. The next section describes what happens if that particular condition is met. This section is the first place to focus — since most new chatbots are built with some “pre-built” intents, entities and dialog nodes. They also have text that may not match what you want to say, or how you want to say it. Take this example from my earlier IBM Watson Fueled Chatbots post. Look at the dialog node titled “Naming of virus”. There is a long description of the virus name and origination — you might want your response to be a bit more brief. Go ahead and change it. Then use the “Try it” button to open a dialog and test out your change. Type in “why is it named covid-19”, and you should be able to see what your modified response looks like.
When dealing with chatbots where you may have reused the data and settings from an earlier chatbot, it is often best to step through each node in a dialog tree, and assess the answer being given back to the end-user. Is this answer accurate for my end users? Does it fit in with the overall approach of my chatbot? Is the personality of my chatbot maintained? For some tips on effective chatbots (and the importance of chatbot personality), check out some of the articles on chatbot success factors and best practices, like Conversational Assistants and Quality with Watson Assistant — Revisited and Best Practices for Building and Maintaining a Chatbot.
Pro Tip: When writing out the text in a Dialog answer, use the markup language instead of HTML tags. Markup language will get processed properly by speech engines and integrations, while HTML tags will not always be properly processed. For example, use “you should **ALWAYS** wash your hands”, instead of, “you should <B>ALWAYS<\B> wash your hands”.
What Do Our Users Want?
The next steps to take in doing some ongoing improvements and changes to your chatbot involve making some changes to what you are answering. What types of questions are your end-users asking? What new conditions have come up, conditions that need to be addressed? Do we have changes to existing guidance offered by our chatbot?
In order to find the answers to these types of questions, we’ll take advantage of something that is unique to Watson Assistant Plus editions, and not available in the traditional Watson Assistant instances. We’ll utilize the analytics associated with the operation of our chatbot.
Go into your Watson Assistant instance, and then drop into the dialog skill that you are interested in. Then click on the “Analytics” option in the left-hand nav bar. You will see a screen that looks similar to the one shown below. It has a few graphs and some interesting looking data. What should we focus on?
Sample of the Analytics Overview Page
I tend to look in certain areas when I initially begin to look at chatbot performance.
Weak understanding — this number represents the user statements that our chatbot had a tough time figuring out. This is an obvious area to address.
Active Users — this number shows how well your solution is being adopted. It can also help you anticipate any increase in costs.
Top Intents and Top Entities — these two lists give you an idea of the areas that are being hardest hit with questions. You can see what people are asking about most often, and track how this might change over time.
When looking at the weak understanding box, click on the “View Logs” link. This will now show you logs of all of the conversations that your chatbot has had over some time period. The first view you see here is filtered — it is showing conversations where no entities were detected, and where no intent was detected. These are the questions that you had no answer for. When looking to improve your chatbot, this is typically a great place to start. See what kinds of questions are NOT being answered.
Gather a list of the legitimate questions that you want to address. Begin to organize these into rough groups. See if there are patterns. Some things that you should be looking for:
Do you see 5 to 10 different user inquiries that are asking the same basic question? That is a candidate for a new intent.
Do you see some new terms showing up a lot? Are particular slang terms beginning to emerge? These can be added to existing entities, or could be new entities that you want to identify.
Do you have any user questions that should have gone to a particular intent, but didn’t? Those user statements (commonly referred to as utterances) should be added to the training data.
What Types of Things Do We Deal With?
OK — so you have determined some areas and types of questions that you are not answering. How do we want to address these things?
The first thing to do is to improve your existing intents. You should add user utterances that were incorrectly classified, or went unclassified, to the training data for the intent that they should map to. The only exception to this is if you have more than 40 examples already in place for a particular intent. In that case, you should begin to remove similar utterances from the training data for that intent — for each one that you add, you should remove one. Otherwise, you may run into the problem of overfitting.
The next thing to do is to identify any new entities that you have. I suggest that you recognize and get these entities defined in your Watson Assistant dialog skill. After you have added these entities to your dialog skill, you should then go and begin to look at the individual dialog nodes that you already have, and determine if any of your responses can be modified to provide more targeted information based on these new entities.
Then it’s time to look at your potential new intents. Keep in mind that one or two utterances about something odd is NOT an intent. For any quality intent that you want to create, you should have at least 10 utterances. You should also check those utterances and make sure that they do not “collide” with, or replicate, utterances associated with other already existing intents.
The most simple scenario to handle with a dialog node is just directly answering a question linked to some intent. Here is an example:
Simple dialog node
At the top of the node you can see it’s name. The name is only important as a way for you to identify a node — what it does, what it answers. Make it brief, but descriptive. Next to the name is the “Customize” button, we won’t be using that for our simple dialog node example. Next is the trigger condition — in this example, we are triggering on “#Whythename”. The hashtag in front indicates that this is an intent (#intent), if there was an ampersand in front of the name then we would be referencing an entity (@entity). You can link intents and entities, as well as context variables, to further refine your dialog node actions. Since this is a simple dialog node, we’ll just trigger on a single intent. Finally, there is the response section. This response is just some text, but responses can take many forms. Try to keep your text responses simple and brief, you want to help your users, not bury them with details.
Now We Know Everything, Right?
Not really. You know how to do some of the easier things to improve your chatbot. At this point, you need to begin implementing some automated testing of your chatbot. This will help you get some objective measures of chatbot accuracy and performance, and will also give you some insight into areas where your chatbot could stand to improve.
So the next thing to do is to implement some automated testing and the application of some DevOps principles. We’ll save that for our next blog post.
With Covid-19 and all of the changes in our world, it’s interesting to think about how this impacts businesses all over the world. What can a chatbot do that is relevant in our new situation — with stay-at-home orders, social distancing, and the closing of non-essential businesses? Chatbots can do something that most local organizations need — communicate to your customers and users with a limited amount of staff, on a 24×7 basis. How many businesses are unable to staff call centers — wait times on many “help lines” can be up to two hours long. Chatbots enable you to “talk” with your customers and give them the information that they want, when they want it. Coupling your chatbot with some speech services can give you a solution that will work for your call center. A recent study by Forrester Consulting indicated that companies saw a benefit of over $5 per contained conversation handled by Watson Assistant. So what are you waiting for?
The Objections
A lot of companies and organizations would consider chatbots, and other AI technologies, but they feel like those technologies are too expensive. They might feel like they don’t have the expertise to build a chatbot on their own. They might have been right — in the past. This week, IBM announced it’s “Citizen Chatbot” campaign. This is targeted at government, healthcare, and other public agencies, and it provides for 90 days of FREE access to a fully functional chatbot with Covid19 information and guidance that is kept up-to-date with content from the CDC. It allows you to quickly and cheaply try chatbot technology to see how it can work for you.
That sounds wonderful, but a lot of my readers are not in those industries. Even though they are not in those industries, they still have a need to share Covid-19 information with their employees, business partners, and customers. Some of this information might be specific to the products and services that they provide. So if I can’t qualify for the “Citizen Chatbot” campaign, is there a way that I can do this for myself? How much effort would it be? How much would it cost?
The Simple Chatbot Solution
The most simple chatbot has three components: Watson Assistant, Watson Discovery, and your web page or mobile application.
Simplified chatbot architecture
Your Web page or mobile application will be the user interface for your chatbot. It is what the user interacts with. It handles login (if needed), sets the colors and presentation of your application, what the forms look like, and all of the things that your end-user sees.
The Watson Assistant piece will drive the “conversation” with the user. It determines what the user wants, and then provides the needed information or guidance. It handles the more simple questions, the frequently asked questions (FAQ’s) and the general questions that your end users may have.
When questions get too specific for the Watson Assistant to handle, they are passed off to the Watson Discovery service. The Discovery service will read (or ingest) a series of documents or web pages, and will then search through this knowledge base (commonly called a corpus) for answers to those more complex or complicated questions. These are often referred to as “long-tail” questions.
Mobile applications may use the Watson Speech services to provide voice inputs and outputs to your Watson Assistant service. These additional services would incur an additional cost when deployed, but would allow your chatbot to relieve some call center volume (which could be a huge cost savings for you). These are not necessary for a chatbot deployment, but represent the ability to add some capability to your chatbot.
The Simple Chatbot Costs
The costs for any solution can be difficult to fully quantify. What will the software development costs be? How much maintenance will need to be done? How many people have to be on call to support the solution? I don’t know your labor and development costs, so I leave the calculation of those costs to you. What I DO know, is the cost of the Watson services that a chatbot would use. So let’s look at our simple chatbot.
We use a single Watson PLUS instance. This single instance is going to cost $120 for each 1000 unique users every month. So if our bot is servicing 10,000 users in it’s first month, with some of them calling in multiple times, my monthly cost for the Watson Assistant service is going to be $1200.
We use a single Watson Discovery instance. We were careful when creating our Discovery instance, and created an x-small size instance, since our knowledge base is less than 50k documents. This size Discovery service will cost us $500/month.
We will use a single Speech-to-Text instance. These instances get billed based on usage — at a rate of $0.02 per minute. If your usage gets really high, this price per minute will drop (see the Speech-to-Text catalog page for service pricing). If we assume 5 minutes of speech per call (which is a loooong call…), we would have a bill of about $1000 for the month.
We will use a single Text-to-Speech instance. These instances get billed based on usage — at a rate of $0.02 per thousand characters. If your usage gets really high, this price per thousand characters will drop(see the Text-to-Speech catalog page for pricing info). If we assume 4000 characters of speech per call (which is a lot of talking in each call…), we would have a bill of about $800 for the month.
Note: IBM currently provides a Watson Assistant for Voice Interaction (WAVI) bundle of Watson Assistant (WA), Watson Discovery (WDS), Speech-to-Text (STT), Text-to-Speech (TTS), and Voice Gateway (VGW) for only $650 per month!! It provides 10K minutes of call time and 10K unique monthly web users.
With all of these costs added together, we see a total monthly cost of $3500 per month (Assistant’s $1200 added to $500 for Discovery, and $1000 and $800 for the Speech services) for handling these 10000 unique customers. That sounds like a lot of money, and it is. But now let’s look at the benefits that we will see.
I am going to make some assumptions, and use some “typical” numbers. I encourage you to do the math shown here, using your own numbers and costs, and see what your particular situation is. For this example, I will assume that we have a local call center that has been struggling to handle those 10,000 calls a month. We’ll assume that our chatbot can deflect only half of those calls (most customers report far more significant call deflection rates). So we figure that 5,000 calls a month are deflected. Let’s take that $5 per deflected conversation benefit from earlier, and we can cut it in half — to $2.50 — let’s be conservative in our benefit estimates. Multiplying those, we end up with a benefit of $12,500. Keep in mind that we are trying to be conservative with our benefits calculations. Now all of sudden our $3500 a month in costs doesn’t look so bad — in fact we have a net benefit of $9000. If we ramp up with more users per month, the costs, and benefits, increase on a fairly linear scale.
Note: Simple math with the WAVI bundle mentioned above also results in a fairly linear cost/benefits curve, with only $650 in costs against a benefit of $12,500 for those first 10000 users.
What Happens Now?
Many of you will be wanting to start building out your chatbot, but your organization will want to manage the effort and will want some sort of project plan. So let’s give them one — and begin to set expectations.
Sample Chatbot Project Plan
Looking at this simple chatbot project plan, you can see the high-level tasks involved in rolling out a chatbot. Provisioning resources is simple, and you can do all of your provisioning in an hour or less. Keep in mind that you should do this within a functional IBM Cloud account (see my post on Getting Started Right With the IBM Cloud).
You then will need to do some work on your dialog skill, first importing some pre-built intents, training data, and dialog flows. This is simple and should take less than an hour. Updating these to fit your particular situation will take a couple of days.
In parallel to your dialog work, you can create a search skill, and implement and execute the web crawling needed to populate your knowledge base for handling those “long tail” questions. All of these tasks can be done during the first few days of the project.
At this point you will need your existing development and operations teams to work together in getting the code to deploy your chatbot widget added to your homepage (or wherever you decide to host your chatbot). I strongly suggest doing this in some kind of staging or test environment. You can then test in this environment, and eventually deploy these changes to your production environments. All in less than a week.
After this initial deployment, you will have some longer workstreams to execute on, as well as some recurring work. For the recurring work, you’ll need to set up some iterative process for reviewing your chatbot results, and identifying improvements that need to be made. You will need to identify areas where things are changing, and where your chatbot needs to change.
The two longer workstreams that are needed to be done involve the automation of the testing process and the automation of the promotion/deployment processes. These are necessary if you want to make your chatbot a smoothly functioning and updating application. The processes and tools that you use here should be able to be used for ANY chatbot that your organization creates.
Let’s Get Ready for Building!
Before I launch into an overview of the steps that you need to take to build your very own Covid-19 Chatbot, you should be aware that there are now hundreds of “How to build your own Covid-19 chatbot” articles out there. Many of them are really good, and you might prefer their approach to what I have here. So I’ll list a few of them here — take a look at them and use the approach and the instructions that seem most straightforward to you.
Simple Covid-19 Chatbot — a great video that shows you how to do this in 4 minutes. He moves fast, so you’ll probably have to start/stop the video quite a bit, but it is simple and straightforward.
The first thing to do is to get a “functional account” in your domain. We touched on this earlier (my post on Getting Started Right With the IBM Cloud), but it is important. Make sure that your “owning” account is not tied to a single person — but is more of a service account that multiple people can administer.
Using that “functional account”, get yourself an IBM Cloud account. Some organizations may already have an IBM Cloud account, in which case, you’ll just need to make proper arrangements with the account owner.
We need to do some basic housekeeping and setup of our Cloud account, so we can more easily manage access to resources. We’re going to want to create a new Resource Group and some Access Groups for our chatbot project. So first go to “Manage -> Account”, and then click on “Resource Groups”. You’ll see a list of resource groups (maybe just a default resource group — it depends on if this is a new or existing account). On this screen, we’re going to click the blue “Create” button to create a new Resource Group.
Resource Group creation
Call your new Resource Group something informative, like “Covid_Chatbot_RG”. Make sure to add the “_RG” at the end of your Resource Group name — it’s never to early to start using naming conventions.
Now we want to create some Access Groups — so we can easily add users to our project based on the role that they will play. So select “Manage -> Access (IAM)”, and then click on the “Access Groups” option. Now we’ll create three access groups, and name them with our resource group name, followed by the role, and then an “_AG” (which shows they are an access group). So for my three access groups, I created the following: “Covid_Chatbot_Admins_AG”, “Covid_Chatbot_Developers_AG”, and “Covid_Chatbot_Users_AG”. After creating these access groups, you should now see them on the “Access Groups” page.
The last thing to do with access groups is to set permissions for the access group. From the “Access Groups” page, click on the “Covid_Chatbot_Admins_AG” group (or your equivalent). Once the access group comes up, click on the “Access Policies” tab, and click the blue “Assign Access” button. This screen is where we will assign access for each of the Access Groups. Here is what you need to set for each group:
For the “Covid_Chatbot_Admins_AG” group:
Select “IAM Services”, “All Identity and Access enabled services”, “Resource group: Covid_Chatbot_RG”, and “All regions”. Check every box for platform access (Viewer, Operator, Editor, Administrator), service access (Reader, Writer, Manager), and resource group access (Viewer, Operator, Editor, Administrator). Note that you may need to click on the “View All” link to see all of your options for resource group access. Press the “Add” button at the bottom of the window.
Select “Account Management”, and “All Account Management services”. Check only two boxes for platform access (Viewer, Operator), and press the “Add” button at the bottom of the window.
Finally, press the blue “Assign” button in the right-hand pane, to assign these permissions to this access group.
For the “Covid_Chatbot_Developers_AG” group:
Select “IAM Services”, “All Identity and Access enabled services”, “Resource group: Covid_Chatbot_RG”, and “All regions”. Check two boxes for platform access (Viewer, Operator), and three boxes for service access (Reader, Writer, Manager). Press the “Add” button at the bottom of the window.
Select “Account Management”, and “Support Center”. Check three boxes for platform access (Viewer, Operator, Editor), and press the “Add” button at the bottom of the window.
Finally, press the blue “Assign” button in the right-hand pane, to assign these permissions to this access group.
For the “Covid_Chatbot_Users_AG” group:
Select “IAM Services”, “All Identity and Access enabled services”, “Resource group: Covid_Chatbot_RG”, and “All regions”. Check one box for platform access (Viewer), and one box for service access (Reader). Press the “Add” button at the bottom of the window.
Select “Account Management”, and “Support Center”. Check three boxes for platform access (Viewer, Operator, Editor), and press the “Add” button at the bottom of the window.
Finally, press the blue “Assign” button in the right-hand pane, to assign these permissions to this access group.
The last thing to do with access groups is to set your account into the admin access group. From the “Access Groups” page, click on the “Covid_Chatbot_Admins_AG” group (or your equivalent). Once the access group comes up, click on the “Users” tab, and click the blue “Add users” button. Now select your account, and then click the blue “Add to group” button.
Now we want to people invited to our project area. Select “Manage -> Access (IAM)”, and then click on the “Users” option. Now you can add the email addresses (IBM Cloud accounts) of the people you want to invite to your effort by clicking on the blue “Invite users” button. Add your users in groups, separated by commas, and assign each group of email addresses to a particular Access Group by clicking the blue “Add” link for the access group that you want assigned. This will give our new users access to the areas of the account that they need access to.
Now we’ll log in using your own account (in my case that might be tox@ibm.com), so we can check out these changes and get started with our chatbot.
Getting Services Created
The first steps in this project involve getting your Watson services deployed. We’re going to try to keep this as simple as possible — if you are looking for more complex deployment examples, see some of the links in the previous section, or reach out to me and I can probably point you in productive directions.
Now we want to make sure that we create these in the right place, so log into the IBM Cloud in your own account, and switch your context to be operating in the newly created account (where the chatbot is going to “live”). To make this context switch, go to your top bar, and look for your current context. Click on it and then choose the more general account (and not your personal account).
Switching your IBM Cloud context
Now let’s start creating some services. Click on the “Catalog” menu option, and then look at the AI offerings. You will want to choose the Watson Assistant. When you click on it, it will give you some options for creating your service. You will want to choose a geographic region (or data center) where you want to create your instance. Choose one that is relatively close to your physical location. Then select a plan (choose the Plus plan). Now you will need to select a service name (I have a naming convention, so mine is called “WA_Covid_Chatbot_Tox”) and a resource group (we’ll use the one we created, “Covid_Chatbot_RG”). Once this is all set, click the blue “Create” button in the right-hand pane to create your Watson Assistant instance.
While this instance provisions, let’s go and create the service that will handle our long-tail questions. Click on the “Catalog” menu option, and then look at the AI offerings. You will want to choose the Discovery service. When you click on it, it will give you some options for creating your service. You will want to choose a geographic region (or data center) where you want to create your instance. Choose the same one as before. Then select a plan (choose the Advanced plan). Now you will need to select a service name (I have a naming convention, so mine is called “WD_Covid_Chatbot_Tox”) and a resource group (we’ll use the one we created, “Covid_Chatbot_RG”). Once this is all set, click the blue “Create” button in the right-hand pane to create your Watson Discovery instance.
Begin Doing Some Assistant Work
So now we are ready to do some damage. Let’s go to our IBM Cloud dashboard, and take a look at our services/resources. Under the services section, you should see the two services that you created. Because you were smart and you used naming conventions, you know by the names what services these are. So you click on the name beginning with “WA” to open your Watson Assistant instance. Once it opens, click on the blue “Launch Watson Assistant” button. You’re now into the Watson Assistant tooling.
You now see a page with your Assistant displayed, it’s called “My first assistant”. You can see that it has one skill, under Dialog, called “My first skill”. Click on the second icon (called “Skills”) in the left-hand nav bar to pull up your list of skils. You should now see a list of all of your skills (you only have one right now). Click on the three dots in the upper right-hand corner of the skill, we need to rename this thing. Select “Rename”, and then rename your skill to “My Covid Skill” or something similar. Now just click on the main area of the box — and you are now in the skill tooling.
Starting off with a new skill
We’re starting from scratch, but we want to get this deployed quickly. So lets grab some work and data that has already be done for us. Click on the three dots in the upper right of the display, and select the “Import” option. We are going to import some JSON that represents a pre-built generic Covid-19 chatbot. You will want to use the fileCovid Json as the import file.
Note: You need to download this file to your local machine to make this work. If you’re not a GitHub person, then just click on the “Raw” button, and a new page will be displayed in your browser window. Once this is displayed, in your browser, just save the page by clicking “File -> Save Page as…”, and save it in JSON format. This content is from the “Call for Code” competition.
You can now poke around and take a look at the Intents that were added to your skill. Also take a look at the Entities, and the Dialog nodes. Don’t change anything — but look around at things. Take some time, after the import of new data your Watson Assistant needs to take some time to learn (called training) all of the new things that you just threw at it. After a couple of minutes, you can click the “Try it” button, and you should see a pane open up on the right-hand side of your browser. This is where you can test and check out your chatbot. Make sure that there is no flag that says that Watson is still training — and then try typing in some questions for your chatbot.
After that, you can now take a look at some of those dialog nodes. Click on one of them — and see how the responses are returned to the end user. Those conditions, the ones with the hashtag in front, are based on your intents. Those conditions with the @ in front of them are based on your entities. Using the detected intents and entities, we determine what the end user is asking about, and we supply an appropriate answer to them.
Let’s Look at the Long Tail
Now we need to configure our other service, the Watson Discovery service. Let’s go to our IBM Cloud dashboard, and take a look at our services/resources. Once again you smile a little, you were smart and you used naming conventions, so you click on the name beginning with “WD” to open your Watson Discovery instance. Once it opens, click on the blue “Launch Watson Discovery” button. You’re now going to see the Watson Discovery tooling.
The first thing that we need to do is to create our Discovery environment and choose a size for our Discovery instance (for more details on this, check out my blog post on Watson Discovery at the Size You Want). So in your Discovery window, find the environment icon (in the upper right corner), click on it, and then click on the blue “Create environment” button. The size of your environment is important — it determines the cost of your Discovery service. We wanted a quick chatbot, and we don’t anticipate a huge amount of documents needing to be stored, so we choose the “X-small” option, under the development tab. Once you find this option, click on the blue “Set up” button to begin setting up your environment, which will take a few minutes.
Once your environment is all set up, we’ll set up a simple collection and a simple web crawl to populate that document collection. You’ll notice that there is a button to “Connect a data source”. Press it. Next we’ll select “Web Crawl” as our data source. Now we get to add URL’s of the sites that we want to crawl for information. Let’s begin with these sites (you may want to add your own information sources):
As you add these URL’s, click the small gear icon that comes up with them as they are added. Then configure the number of hops for each data source. The web crawl will follow links, and links from those pages, and this is the depth of that link following that will be used. Once you have added your sources, click on the blue “Save & Synch” button. This will start your initial web crawl of data.
Your web crawl has now begun. On this screen, you can see the name of your new collection, which is just some random collection of characters. We want to change that. We’re going to call our new collection “CDC_Covid_info”, so click the small pencil icon next to that random name, and change it. So now we’ll just wait for those sites to get crawled and processed. This takes a little while, so we’ll just continue on and let it continue in parallel.
Note: Don’t panic if/when your Discovery web crawl begins to display numbers of documents that have failed. Documents fail to get ingested for a number of different reasons. Usually we just cast a wide net, and if some pages don’t get properly picked up, then we’ll find that information somewhere else.
Bringing it Together
Check your watch — how long has it been since we started? All we need to do now is connect these services, and then deploy our chatbot. Let’s start by connecting the services.
Go back into your Watson Assistant instance, and let’s look at the Assistants, which you can see by clicking on the top icon in the left-hand navigation pane. You can now see your only assistant — click on the rectangle to go into the assistant. Once inside, you can see your recently imported dialog skill. If you click on that box, you go to that familiar tool where you can modify your intents, entities, and dialog nodes. Let’s not do that right now, instead let’s look BELOW our dialog skill. There is a space there for a Search Skill. Let’s go and add one of those!
So press the blue “Add search skill” button, and you will be asked to name your search skill. Let’s name it the same thing as our document collection in Watson Discovery (“CDC_Covid_info”)— that way we won’t be confused or misled later — and hit the blue “Continue” button. On the next screen we need to choose our Watson Discovery instance (“WD_Covid_Chatbot_Tox”), and our web crawler collection of documents (“CDC_Covid_info”). Then press the blue “Configure” button, which will bring you to the last configuration screen. This screen allows you to tailor the information returned to your user. For now, we’ll just stick to the default values. So the last thing to do is to press the blue “Create” button to establish the collection as your source for answers to any “long tail” questions that your chatbot gets.
Now we need to make sure that we can get there. So let’s go into the Dialog skill for our chatbot. Scroll all of the way to the bottom, and find the dialog node called “Anything else”. Click on this node and open it up. If you look at it, it basically states that if it sees anything not covered in your intents, it will fire off a webhook. We don’t have time to do webhooks today (see some of the great materials on webhooks by Mitch Mason, Morgan Langlais, or this simple webhooks demo), so we’ll get rid of this. Click on the minus sign next to all but one of your options under the “Assistant Responds” section. For the last remaining option, click on the small gear, and configure the response. Under the “If assistant recognizes”, the value should be “anything_else”. Under the “Assistant Responds” area, select “Search skill”. Once you’ve done this (which tells the chatbot if it cannot detect an intent, to send the question to the search skill — or Discovery collection), press the blue “Save” button.
Testing and Releasing
Your chatbot is done — it might not feel that way, but it is. So let’s check it out. First we’ll want to do a basic check out of the answers to common user questions. So go back into your Watson Assistant instance, and let’s look at the Assistants. You know how to get there by now — if you don’t, then scroll back up and figure it out again.
Click on your assistant, and in the upper right of the window, you will see a section for integrations. In that section is a link that says “Add integration +”. Click on that link.
You have a few options here, but right now we’re just going to use the “Preview link” option. This is there so you can test out your chatbot with a private link, before you have to expose it to the world. So click that option, and just leave the default name for your preview link, and press the blue “Create” button. Once you do — a link will be generated that will allow you to see your chatbot in a web browser. Click on that link and ask some questions, check out how your chatbot is working.
When you are ready — and you should be ready pretty quickly — it’s time to deploy this chatbot. Let’s go back to the assistant page, and click on that link that says “Add integration +” again. This time we’ll add a “Web chat” integration. So click that option, and again we will just leave the default name for your name, and press the blue “Create” button. Now we see some options to customize your UI, and as you change these options you can see a sample window to the right of what your chatbot will look like. Once you are done playing with the title bar name and the colors that you want, you can just copy the code in the following section, and have it added to the web page where you want your chatbot. Simple as that.
Now What?
Now you need to update this chatbot. You should learn more about how Intents work, how Entities can be used to help make your chatbot responses more targeted, and how Dialog nodes work. I like the Watson Assistant landing page (full disclosure — I help curate this page) for links to more learning resources. You can also take some of the online courses offered if you prefer to learn that way. The BEST way to learn is by doing. Make changes to your chatbot and see how it reacts. Don’t be scared — if you ever want to try something out, just duplicate your skill first, and then work on the copy. Then you always have the original to go back to if you really screw something up.
You will want to make sure that your responses haven’t needed to change due to changing conditions, and you may want to add new intents for new situations that your users might ask about.
If you’re really stuck, or just overwhelmed with the possibilities, you can always reach out to me. I can help you talk through your options, and I can give you some good advice to move forward with. Just reach out to me at dtoczala@us.ibm.com.
Dan Toczala's Blog 