Security Details: Serverless database access within IBM Watson Conversation service

Slackbot Architecture

Last week, I introduced you to a new tutorial for a database-driven Slackbot. Today, I am going to discuss details of how the IBM Watson Conversation service is accessing a Db2 Warehouse service from within a dialog. It uses a serverless setup with IBM Cloud Functions. All the necessary credentials to execute the code and to access the Db2 database are automatically bound. Hence, the function code and the dialog don't need any account-specific changes and are generic.

New tutorial: Db2-driven Slackbot

Ever wanted to build a Slackbot, a chatbot integrated into Slack, on your own? I am going to show you how easy it is to integrate Slack or Facebook Messenger with the IBM Watson Conversation service. As a bonus, the bot is going to access a Db2 database to store and retrieve data. The solution is based on IBM Cloud Functions and entirely serverless

Slackbot Architecture

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Easy Database Setup the Serverless Way

Serverless Slackbot with Db2

A tutorial I wrote, featuring a database-backed Slack chatbot, is now live. It uses Db2 as database system to store event data. The client accessing the database is written in Node.js and is implement with IBM Cloud Functions in a serverless way. During the development of that tutorial I faced the question on how to perform the database setup. Should I guide users through the user interface to create a table and insert data? Should they install a Db2 client and execute a script locally? I solved the problem in a serverless fashion. Here are the details.

Chatbots: Some tricks with slots in IBM Watson Conversation

As you might remember, I have been using the IBM Watson Conversation service and DB2. My goal was to write a database-driven Slackbot, a Slack app that serves as chat interface to data stored in Db2. I will write more about that entire Slackbot soon, but today I wanted to share some chatbot tricks I learned. How to gather input data, perform checks and clean up the processing environment.

Slots

With my chatbot interface to Db2 I want to both query the database and insert new records. Thus, I need to collect input data of various kind. The Conversation service has a neat feature named input slots that simplifies that process. Within a dialog node (a logical step within the chat flow) I can specify a list of items the Conversation service should check for. I can tell in which variable to save that input and what question to ask if that data was not provided yet. Optional slots, i.e., optional data, can be enabled.