Building a digital app is easy with the IBM Digital App Builder tool. Using this tool, a citizen developer can quickly build a smart app which contains the basic building blocks, connect to microservices, and embed AI services with ease.
Recently, I introduced you to a new tutorial for a database-driven Slackbot. Today, I am going to discuss security details, 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.
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 code in the tutorial uses a serverless fashion with IBM Cloud Functions.
If you follow my private blog you might remember that 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.
2017 has been a year of transition, especially in the realm of exciting new technologies. We're wrapping up 2017 with your top picks from the year, including announcements, how-to's and innovative use cases, to help you prepare for what's to come in IBM Cloud next year.
Imagine, you are in a conversation with a chatbot and you feel that the human angle is completely missing because the bot starts it's dialog with a usual "Hi" or "Hello". You may want to personify the conversation by adding the name of the person (who's logged in) to the boring "Hi" or "Hello". Ever thought of this? It's not just personification, How about wishing appropriately based on the time of the day someone invokes your chat application? Also, how about passing values back and forth during a conversation between the nodes or from application to a node?
Creating and maintaining chatbots in multiple languages can be costly, error-prone, and not easily scalable. Each change in a bot needs to be manually replicated across each language the bot supports. The manual nature of maintaining multilingual chatbots can have a real impact on continuous delivery as the language-specific changes must be performed by language experts and this takes time. Moreover, we need to pay for every single change – intents, entities, and bot output. This can get expensive very quickly — so, what's the best way to translate chatbots into different languages without impeding continuous delivery and disrupting DevOps?
More and more chatbots are being developed and there are good reasons for it. Not all chatbot projects succeed. Often, missing user acceptance is stated. The dialog system might not have hit the nerve, might not have fitted into the target environment. Would you talk with a friend who does not remember your name is repeating the same five phrases over and over again? I would not. So what can be done to make chatbots more lively, more human-like? Here are some best practices and ideas on how to implement them.