Exploring Google Dialogflow through a UC Berkeley Transit Assistant

Google’s Dialogflow is an incredibly easy platform to explore natural language processing and voice/text-based assistants at a high level. To explore what the platform could do, I created a basic agent focused on answering queries related to UC Berkeley’s intra-campus bus system – Bear Transit.

To configure the agent, I defined two entities - bus-line and station. Here’s what that process looks like in the Dialogflow console.

Then, I created several training phrases for the Bus Departure agent. Dialogflow automatically recognized the entities from my phrases based on the hard-coded entities defined above.

To fulfill these requests, Dialogflow requires a Webhook that takes in a JSON payload and returns a response string to speak to the user. I built my fulfillment engine on AWS Lambda via a lightweight Python Script that made a request to Bear Transit’s official API.

Check out the code here.