Create a cognitive retail chatbot
Build a configurable, retail-ready chatbot
As a Python developer, you can use this pattern to learn how to add features such as a shopping cart, context store, and custom inventory search into your chatbot. When you’ve completed the pattern, you will understand how to create a chatbot dialog using Watson Assistant, a Cloudant NoSQL database, Watson Discovery, and a Slack group.
Chatbots are a hot topic in the retail industry, but so far the execution has mostly amounted to little more than a novelty experience for customers. Interested in adding a chatbot? In this developer code pattern, learn how you can create an easily configurable, retail-ready Watson Assistant-based chatbot that lets a user find items to purchase and then add and remove items from their cart.
- The user writes a message to the slackbot.
- The slackbot uses the Watson Assistant service to let users search, add, or remove products from their cart.
- The Watson Discovery service provides users with a list of items to add or remove from their carts.
- The user and cart data is stored in the database.
Find the detailed steps for this pattern in the README file. The steps show you how to:
- Clone the repo.
- Create IBM Cloud services.
- Get IBM Cloud credentials and add to the .env file.
- Configure Watson Assistant.
- Configure Watson Discovery.
- Configure Slack.
- Run the application.