Now that you have created a solution for your bot, it's time to add your first dialogue. In Teneo, all dialogues are designed as flows. Flows contain the logic needed to resolve a specific question of the user. They can be simple (e.g. a flow that answers the question "What kind of coffee do you have?") or complex (for example, a flow that helps the user order a coffee beverage), but they always represent some kind of pre-defined conversational pattern.
Throughout these pages, we will create a bot for Longberry Baristas, a very hip (yet fictitious) coffee bar in New York. We will start with a simple flow to handle the following conversation:
User: What kind of coffee do you have?
Bot: We offer a variety of handcrafted coffee drinks - from espressos to macchiatos to flat whites and more.
To achieve this we will do the following:
The result will look like this:
Before you continue, make sure you are logged in to Studio and have opened the solution you created earlier.
Flows are stored in folders. In the 'Explore' section on the left, you see several folders with flows that came out of the box. These flows make sure your bot will understand and respond to greetings, meta-requests (e.g. 'Can you repeat that?'), feedback (e.g. 'You are great') and conversational Smalltalk (e.g. 'How are you?'). In that way, since you are the owner of this solution, you can change the answers in these flows if you wish.
We will store our flows in a separate folder, so let's create that first.
Flowsand hit Enter.
You can add as many folders and subfolders as you like and you can structure them any way you like. This is particularly handy for bots that handle multiple domains.
Now that we have a folder in which we can store our work, we can create our first flow:
User asks about coffees we offer.
As you can see, flows are represented graphically in Teneo. Simple flows just contain a trigger that recognizes the user's intent (the blue box at the top) and output that contains the bot's answer (the purple box at the bottom). More complex flows contain more elements like multiple triggers, scripts, different outputs, etc. We're going to start with a simple flow, so you will only need to populate the trigger and the output that you see in the flow graph.
Triggers are used to recognize inputs from the user that express a similar intent. In this trigger, we want to capture and understand the user's intent around which coffee Longberry Baristas serve. It is important to recognize that there can be several ways of expressing the same intent. For example, 'What coffee do you sell?' or 'Which coffees do you serve?'. To make the trigger match on user input, we need to add a match requirement. This requirement is essentially a criterion that needs to be met for an input to be matched.
Tell me about your coffeeby selecting 'Add name' on top of the trigger configuration panel.
What kind of coffee do you have?, then hit the 'plus' icon beside it or hit Enter.
You might have noticed that the trigger has a yellow 'TODO' label on it, this means that the trigger does not contain a defined match requirement yet. So, let's start with populating the trigger with example inputs and adding a match requirement. Hovering over the 'TODO' label will give you more info on what's missing.
Now, you can repeat the step above to add more examples one by one, but you can also paste multiple examples in one go:
Do you have brewed coffee Do you serve macchiato Do you have cappuccino Tell me about the coffees you have Tell me about your coffee What coffee do you serve? What kinds of coffees do you serve? What kinds of drinks do you sell Which coffees are offered by Longberry Baristas? Which drinks do you have? Which sorts of coffees do you have
When you generate a new class match requirement, the class gets generated based on the training data that you assigned in the examples panel. Once the class is generated, it gets added to the overall machine learning model of your solution. However, if you want to expand your class with additional training data, you have to do so in the class manager. You will learn more about the class manager on a later page.
The positive examples are also used for auto-tests for when you want to automatically test your bot.
Next, we will need to set the bot's answer in the flow. There are many options to define the bot's response. For now, we will focus on the answer text only.
We offer a variety of handcrafted coffee drinks...
We offer a variety of handcrafted coffee drinks - from espressos to macchiatos to flat whites and more.
Now that we have added everything needed to our first flow, it's time to save it:
Adding a description will help you remember the changes you did and will show up in the Flow's version history.
Done! Congratulations! You have created your first flow!
Now it's time to test your work inside Teneo Studio.
What kind of coffee do you have?
Which drinks do you serve?
As you can see, both inputs will trigger your flow. Teneo not only recognizes the exact learning examples you entered but also different variations that express a similar intent.
You have just created your first flow, so you have already seen a few important concepts in Teneo:
Try adding another flow where the user can ask which sizes the Longberry Baristas serve coffee in. The bot should return with the three available sizes we have, small, medium, and large.
Now that we have created our first flow it's time to learn more about transitions.
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