Oracle Digital Assistant is a new name for Oracle Chatbot. Actually it is not only a new name - from now on chatbot functionality is extracted into separate cloud service - Oracle Digital Assistance (ODA) Cloud service. It runs separately now, not part of Oracle Mobile Cloud Service. I think this is a strong move forward - this should make ODA service lighter, easier to use and more attractive to someone who is not Oracle Mobile Cloud service customer.
I was playing around with dialog flow definition in ODA and would like to share few lessons learned. I extracted my bot definition from ODA and uploaded to GitHub repo for your reference.
When new bot is created in ODA service, first of all you need to define list of intents and provide sample phrases for each intent. Based on this information algorithm trains and creates machine learning model for user input classification:
ODA gives us a choice - to user simpler linguistics based model or machine learning algorithm. In my simple example I was using the first one:
Intent is assigned with entities:
Think about entity as about type, which defines single value of certain basic type or it can be a list of values. Entity will define type for dialog flow variables:
Key part in bot implementation - dialog flow. This is where you define rules how to handle intents and also how to process conversation context. Currently ODA doesn't provide UI interface to managed dialog flow, you will need to type rules by hand (probably if your bot logic is complex, you can create YAML structure outside of ODA). I would highly recommend to read ODA dialog flow guide, this is the most complex part of bot implementation - The Dialog Flow Definition.
Dialog flow definition is based on two main parts - context variables and states. Context variables - this is where you would define variables accessible in bot context. As you can see it is possible to use either basic types or our own defined type (entity). Type nlpresult is built-in type, variable of this type gets classified intent information:
States part defines sequence of stops (or dialogs), bot transitions from one stop to another during conversation with the user. Each stop points to certain component, there is number of built-in components and you could use custom component too (too call REST service for example). In the example below user types submit project hours, this triggers classification and result is handled by System.Intent, from where conversation flow starts - it goes to the dialog, where user should select project from the list. Until conversation flow stays in the context - we don't need to classify user input, because we treat user answers as input variables:
As soon as user selects project - flow transitions to the next stop selecttask, where we ask user to select task:
When task is selected - going to the next stop, to select time spent on this task. See how we are referencing previous answers in current prompt text. We can refer and display previous answer through expression:
Finally we ask a question - if user wants to type more details about task. By default all stops are executed in sequential order from top to bottom, if transition is empty - this means the next stop will execute - confirmtaskdetails in this case. Next stop will be conditional (System.ConditionEquals component), depending on user answer it will choose which stop to execute next:
If user chooses Yes - it will go to next stop, where user needs to type text (System.Text component):
At the end we print task logging information and ask if user wants to continue. If he answers No, we stop context flow, otherwise we ask user - what he wants to do next:
We are out of conversation context, when user types sentence - it will be classified to recognize new intent and flow will continue:
I hope this gives you good introduction about bot dialog flow implementation in Oracle Digital Assistant service.
I was playing around with dialog flow definition in ODA and would like to share few lessons learned. I extracted my bot definition from ODA and uploaded to GitHub repo for your reference.
When new bot is created in ODA service, first of all you need to define list of intents and provide sample phrases for each intent. Based on this information algorithm trains and creates machine learning model for user input classification:
ODA gives us a choice - to user simpler linguistics based model or machine learning algorithm. In my simple example I was using the first one:
Intent is assigned with entities:
Think about entity as about type, which defines single value of certain basic type or it can be a list of values. Entity will define type for dialog flow variables:
Key part in bot implementation - dialog flow. This is where you define rules how to handle intents and also how to process conversation context. Currently ODA doesn't provide UI interface to managed dialog flow, you will need to type rules by hand (probably if your bot logic is complex, you can create YAML structure outside of ODA). I would highly recommend to read ODA dialog flow guide, this is the most complex part of bot implementation - The Dialog Flow Definition.
Dialog flow definition is based on two main parts - context variables and states. Context variables - this is where you would define variables accessible in bot context. As you can see it is possible to use either basic types or our own defined type (entity). Type nlpresult is built-in type, variable of this type gets classified intent information:
States part defines sequence of stops (or dialogs), bot transitions from one stop to another during conversation with the user. Each stop points to certain component, there is number of built-in components and you could use custom component too (too call REST service for example). In the example below user types submit project hours, this triggers classification and result is handled by System.Intent, from where conversation flow starts - it goes to the dialog, where user should select project from the list. Until conversation flow stays in the context - we don't need to classify user input, because we treat user answers as input variables:
As soon as user selects project - flow transitions to the next stop selecttask, where we ask user to select task:
When task is selected - going to the next stop, to select time spent on this task. See how we are referencing previous answers in current prompt text. We can refer and display previous answer through expression:
Finally we ask a question - if user wants to type more details about task. By default all stops are executed in sequential order from top to bottom, if transition is empty - this means the next stop will execute - confirmtaskdetails in this case. Next stop will be conditional (System.ConditionEquals component), depending on user answer it will choose which stop to execute next:
If user chooses Yes - it will go to next stop, where user needs to type text (System.Text component):
At the end we print task logging information and ask if user wants to continue. If he answers No, we stop context flow, otherwise we ask user - what he wants to do next:
We are out of conversation context, when user types sentence - it will be classified to recognize new intent and flow will continue:
I hope this gives you good introduction about bot dialog flow implementation in Oracle Digital Assistant service.
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