I made some updates to the flowchart from the last part
- added (Yes)(No) suggestion chips when bot asks a question (this is a Dialogflow Messenger bot)
- added an additional training phrase “Start Over” into intent 1
- changed the name of context 4
Let us translate the first intent – 1 identify.vertebrate
You copy the intent name, obviously.
Then check the input and output contexts. This intent has no input contexts, and it has an output context called await_has_fur. The user training phrase will be “identify vertebrate” and the bot’s response will be “Does it have fur?”
This is how we define the intent
Let us translate intent 2 – hasfur.YES
This intent does have an input context – await_has_fur, but has no output context.
Here is the intent definition.
In the case of intent 3, we have both an input and an output context.
Here is the intent definition
You can do the same for the rest of the intents and fully translate the flowchart into a Dialogflow agent.
Here is what the bot looks like in action
As you can see, if you are fairly rigorous about the flowchart definition, you can translate it into a Dialogflow ES agent pretty quickly.
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"The magic key I needed as a non-programmer" The custom payload generator was the magic key I needed (as a non-programmer) to build a good demo with rich responses in DialogFlow Messenger. I've only used it for 30 minutes and am thrilled. I've spent hours trying to figure out some of the intricacies of DialogFlow on my own. Over and over, I kept coming back to Aravind's tutorials available on-line. I trust the other functionalities I learn to use in the app will save me additional time and heartburn. - Kathleen R Cofounder, gathrHealth