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Chatbot - Visualization service

projectplanchatbotvisualization

Introduction

In the previous blog, we have planned the architecture for the chatbot, and we have chosen Apache Superset as the visualization service. In this blog, we will plan on how to build bridge services to access the superset functions.

Functions Required

For the chatbot, the following functions are required from the superset:

  • Execute SQL query
  • Visualize the data(Create charts)
  • Import Data(Link database or upload files)
  • Show dataset details(columns, etc.)

All the services will be accessed via superset’s REST API. We will build several functions to make sure the client does not directly access the superset API.

Preperation

Step 1: Setup Superset

Option 1: Local Setup

If you are setting up superset locally, I highly recommend to follow this document: Installation - Using Docker Compose

Option 2: Remote Setup

Please refer to Installation - Kubernetes. I ran my own instance on the cluster described in the previous posts; it has since been decommissioned.

Step 2: Choose a dataset for testing

Feel free to use any dataset you get, in my case, I am using London Bike Sharing Dataset from Kaggle.

Step 3: Develop bridge services

Next Phase

In the next phase, we will start to build the frontend for the chatbot, and integrate the serverless functions.

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