LUIS and Bots

Integration Architect and Microsoft Azure MVP um Deloitte
24. Mar 2018
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
LUIS and Bots
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LUIS and Bots

Hinweis der Redaktion

  1. Interested in all things integration – which of course includes MS Flow
  2. Most of these slides are taken from Kent’s presentation at Integrate 2017 USA
  3. Some slides are from Eldert
  4. BOT builder: Full SDK for .NET, Node.js Emulator Sample Bots Bot Framework portal: register,connect and manage bot in an easy and convenient way Includes diagnostic tools and web chat control for embedding on a web page
  5. A user may be typing simple requests based on natural language. For example, a user may type "I want a pepperoni pizza" or "Are there any vegetarian restaurants within 3 miles from my house open now?". Natural language understanding APIs such as LUIS.ai are a great fit for scenarios like this. Using the APIs, your bot can extract the key components of the user's text to identify the user's intent.
  6. Provide important navigation clues Where is the menu? Where to go for help? What is the privacy policy?
  7. Users will not always travel in a linear path
  8. The “stubborn bot”
  9. The “clueless bot” The “mysterious bot”
  10. When implementing natural language understanding capabilities in your bot, set realistic expectations for the level of detail that users are likely to provide in their input.
  11. 44
  12. In this fourth lab, we will be receiving the orders from the business customer’s topic in a new Logic App, and check the total amount of the invoice. In case the customer placed a large order (over $50000), we will create a task for one of our sales employees to contact the customer to verify the order. In case the order is correct, the invoice will be emailed to the customer. The Logic App will then call a function, in which we will check a storage table to determine how much discount the customer will be given (based on the total order amount), and finally will place a file on blob storage, which will be used by an employee to refund the customer.