Aura –

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Mon, 01 Jan 0001 00:00:00 +0000

Users authentication

Description of users’ authentication process in Aura Bot. Discover the different types of users and how they can authenticate in Aura

Introduction

aura-bot supports different types of users, each of them with access to specific services and use cases:

Authenticated users
Anonymous users

In the authentication process, aura-bot carries out the following summarized steps:

Obtains information from the users
Validates it
If the validation is successful, and depending on the type of user, aura-bot allows accessing to different services
Stores the user’s data

The detailed process is described in users’ authentication flow.

The authentication of a user implies the acceptance of Terms and Conditions, which are handled by aura-bot.

In certain scenarios (use cases) where the user is unauthenticated, it is required to redirect specific intents that need authentication. Find here the complete information regarding login redirections.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

Aura Bot conversational flow

Description of users’ authentication process in Aura Bot. Discover the different types of users and how they can authenticate in Aura.

Aura conversational process

Once the user’s authentication is validated, aura-bot starts the message processing.

Firstly, all incoming message middlewares are executed, in the order established in the bot set-up.
Then, recognizers come into play in order to recognize the user’s intention.
Depending on the type of input message, the process is different. For instance, aura-bot is able to recognize the user’s intent itself if the message is an auraCommand or requires summoning Aura Cognitive Services for this purpose.
Recognizers are executed sequentially and, after one of them recognizes an intention, the rest are not executed.
Once the intent and entities are identified, the next step is to route the message to the dialog in charge of that intent for the current channel. This step is done by the main dialog. This special and core dialog is responsible for:
- Getting the dialog that handles the recognized intent for the current channel. If it does not exist, an error is returned to the channel.
- Validating if the user’s context matches with the context filters configured in the selected dialog. If that is the case, then the context filter dialog is executed.
- Validating the authorization settings of the dialog to be executed with the AuraUser authentication model. In the case of Kernel authentication, it checks that the scopes and purposes of the user contain the mandatory ones to execute the dialog.
  This validation improves aura-bot performance, avoiding external requests to Kernel that will return a permissions error. If the user is anonymous, this step is not executed.
The specific dialog selected is started during one of the steps of the main dialog, and thus the execution is now managed directly by that specific dialog.
The dialog must get the information needed to provide a proper response to the user through Aura NLP, Complex Logic API or Kernel Telco APIs in order to generate the expected answer.
The response is now ready to be provided to the user and will start its way through the outgoing middlewares.
After that, the execution flow continues to the next step of the MainDialog, that is checking whether or not the dialog must include suggestions. To do so, it will check that the proper dialog settings are stored in the context. If suggestions are needed, the suggestions API will be called, the resulting activity will be prepared following the UX Guidelines.
In the end, a complete response consisting of several ordered activities will be provided to the channel, that will read them from Direct Line and the result will be rendered to the user.

Conversational process flowchart

The following flowchart schematically shows the components involved in the process.

Check here the complete Aura Bot conversational process flowchart.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

Aura Bot channelData

How Aura Bot manages the channelData property used by Aura to define the Aura request-response semantic model

The current section details the management by aura-bot skill of the channelData property used by Aura to define the Aura Request – Response Semantic Model.

The Aura semantic application model defines a set of properties that can be used as secondary elements of the Microsoft Azure Bot Service and Bot Framework Activities property included in the application activities.

channelData is an open field in the activity, i.e., the message exchanged during a communication between a channel and a bot built on top of Microsoft Bot Framework, that is devoted to define the specific format of the request needed by the specific bot to provide a proper response to the channel and, thus, to the user.

The current section will describe the specific channelData aspects related to aura-bot, such as:

How to handle the different channelData versions.
channelData mapping in Aura response.
channelData validations in requests and responses.