Chatbots can serve a wide variety of use cases like answering frequently asked questions or booking flows. Customer support however often requires a human agent to serve user questions with a high degree of quality. With Airy Core you can get the best of both worlds by using NLP frameworks like Rasa to suggest a set of replies to the agent. This way agents can handle the vast majority of use cases with the click of a button (see screenshot).
- Step 1: Add a custom response type
- Step 2: Update the user stories
- Step 3: Extend the Airy connector
- Step 4: Retrain and restart
The easiest way to instruct Rasa to suggest replies for user messages is by adding them as a custom response type. To do this we add the following block to the
responses section in our
Now we can use this new response type in our
stories.yaml to let the bot know when to suggest replies:
Now we need to update our custom Rasa connector for Airy Core to this response type. For this we extend the send_response method in the Airy connector so that it calls the suggest replies API whenever it encounters a custom response payload:
Now we need to stop the server and retrain the model:
Finally, we start the Rasa server, open the Airy Inbox (at
http://airy.core for local deployments), where we should
see the suggested replies whenever a contact greets us (see gif above).