Hello from serverless messanger chatbot22 Jul 2018
Messanger chatbots are now becoming more and more popular. They can help us order pizzas, ask about the weather or check the news.
In this article, I would like to show you how to build a simple messanger chatbot in python and pass it on AWS lambda. Additionally use the wit.ai service to add to it the natural language understanding functionality and make it more intelligent.
To build the messanger chatbot I will need facebook app and facebook page.
The whole communication is going through a Facebook page thus I need to create it https://www.facebook.com/bookmarks/pages
I will need the page id which you can find at the bottom of your page: https://www.facebook.com/page_name/about
Then I create facebook app. https://developers.facebook.com/quickstarts/?platform=web
I will copy the AppId and AppSecret which will be needed in the next steps:
Then I will add the messanger product and setup it. I need to select page we already created and copy generated access token.
Finally I have to setup the webhooks for messanger product To finish this step I need to setup our chatbot on aws lambda. I also have to provide the verify token which will be used to validate our endpoint.
Now I will prepare my chatbot endpoint. I will setup it on the AWS lambda.
For my chatbot I need to configure API Gateway. I have to choose security open otherwise I won’t be able to call it from messanger
I also need to provide code which will handle the messanger webhook and send response. I will simply put the code in the online editor. Let’s take a look at the code:
Bellow I have to setup environment variables: verify_token - verification token (I use keepass to generate it) which we will use in webhook setup access_token - value from the messanger webhook page setup app_secret - facebook app secret
Now I’m ready to finish the webhook configuration:
I use api gateway url ass Callback URL and verify_token I have just generated.
Natural language undestanding
Messanger give easy way to add natural language undestanding functionality. To add this I simply configure it on messanger product setup page
Here I can choose already trained models but I will go further and I will create custom model. Messanger will create the new wit.ai project for me.
On the wit.ai I can simply add some intents (like: hungry) and additional information which can be retrieved from the phrase (like: I want some pizza)
The messanger/wit integration is very smooth let’s analyze the webhook json I get when I put I want to eat pizza
After wit integration the nlp object was added. Now I can get the recognized intent with some confidence (like: hungry) and additional entities (like: dish).
Finally I can talk with my chatbot :)