Since I’m an AWS fanboy, of course I chose Lambda!īefore diving into the details, let’s look at the architecture that we are trying to achieve. The objective of this article is to simply share the steps that are required to run your Dialogflow bot fulfillment Webhook on AWS Lambda. So, it’s better to host it on some FaaS (Function-as-a-service) solutions since it is ideal for idle workloads like chatbot.
When the number of Intents of the bot increases overtime, it becomes harder to edit in the Inline Editor.
Google Cloud account (Dialogflow agent is tied to a Google Cloud Project)ĭialogflow comes with its own Inline editor powered by Cloud Functions for Firebase for Intent Webhook.The body of this response is a JSON object with the following information. This article expects readers to have the prior basics knowledge about Dialogflow chatbot, AWS Lambda and Nodejs. Once your webhook receives a webhook request, it needs to send a webhook response. This will provide you with basic building blocks that you can extend to build your desired full-fledged chatbot with Dialogflow and AWS Lambda. The objective of this article is to simply share the steps that are required to run your Dialogflow bot fulfillment Webhook on AWS Lambda.
Building Chatbot with Google Dialogflow and AWS LambdaĪfter playing around with Chatbots on all three clouds: AWS, Azure and Google, I’ve found that Dialogflow by Google is relatively easy to build a chatbot due to its friendly UI, detailed and up-to-date documentation, easy and extensive integration to 8 different messaging platforms and availability of pre-built agents and samples that I can take as reference which in turn has hastened my development.ĭialogflow was formerly known as api.ai and acquired by Google in 2016.
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