OVERVIEW
Virtual Assistants
Kore.ai Platform
Key Concepts
Natural Language Processing (NLP)
Accessing Platform
VIRTUAL ASSISTANTS
Virtual Assistant Builder
Virtual Assistant Types
Getting Started
Creating a Simple Bot
SKILLS
Storyboard
Dialog Task
Introduction
Dialog Builder (New)
Dialog Builder (Legacy)
User Intent Node
Dialog Node
Entity Node
Supported Entity Types
Composite Entities
Supported Colors
Supported Company Names
Form Node
Logic Node
Message Nodes
Confirmation Nodes
Bot Action Node
Service Node
Custom Authentication
2-way SSL for Service nodes
Script Node
Agent Transfer Node
WebHook Node
Grouping Nodes
Connections & Transitions
Manage Dialogs
User Prompts
Knowledge Graph
Terminology
Building
Generation
Importing and Exporting
Analysis
Knowledge Extraction
Build
Alert Tasks
Introduction
Ignore Words and Field Memory
How to Schedule a Smart Alert
Small Talk
Digital Views
Overview
Configuring Digital Views
Digital Forms
Overview
How to Configure Digital Forms
NATURAL LANGUAGE
Overview
Machine Learning
Introduction
Model Validation
Fundamental Meaning
Introduction
NLP Guidelines
Knowledge Graph
Traits
Introduction
How to Use Traits
Ranking and Resolver
Advanced NLP Configurations
INTELLIGENCE
Overview
Context Management
Overview
Session and Context Variables
Context Object
How to Manage Context Switching
Manage Interruptions
Dialog Management
Sub-Intents & Follow-up Intents
Amend Entity
Multi-Intent Detection
Sentiment Management
Tone Analysis
Sentiment Management
Event Based Bot Actions
Default Conversations
Default Standard Responses
TEST & DEBUG
Talk to Bot
Utterance Testing
Batch Testing
Conversation Testing
CHANNELS
PUBLISH
ANALYZE
Overview
Dashboard
Custom Dashboard
Overview
How to Create Custom Dashboard
Conversation Flows
NLP Metrics
ADVANCED TOPICS
Universal Bots
Overview
Defining
Creating
Training
Customizing
Enabling Languages
Store
Smart Bots
Defining
koreUtil Libraries
SETTINGS
Authorization
Language Management
PII Settings
Variables
Functions
IVR Integration
General Settings
Management
Import & Export
Delete
Versioning
Collaborative Development
Plan Management
API GUIDE
API Overview
API List
API Collection
SDKs
SDK Overview
SDK Security
SDK App Registration
Web SDK Tutorial
Message Formatting and Templates
Mobile SDK Push Notification
Widget SDK Tutorial
Widget SDK – Message Formatting and Templates
Web Socket Connect & RTM
Using the BotKit SDK
Installing
Configuring
Events
Functions
BotKit SDK Tutorial – Agent Transfer
BotKit SDK Tutorial – Flight Search Sample Bot
Using an External NLP Engine
ADMINISTRATION
HOW TOs
Creating a Simple Bot
Creating a Banking Bot
Context Switching
Using Traits
Schedule a Smart Alert
Configure UI Forms
Add Form Data into Data Tables
Configuring Digital Views
Add Data to Data Tables
Update Data in Data Tables
Custom Dashboard
Custom Tags to filter Bot Metrics
Patterns for Intents & Entities
Build Knowledge Graph
Global Variables
Content Variables
Using Bot Functions
Configure Agent Transfer
Update Balance Task
Transfer Funds Task
RELEASE NOTES
  1. Home
  2. Docs
  3. Virtual Assistants
  4. Intelligence
  5. Context Management

Context Management

Effective context management is important because it allows bots to interact with users in a way that is easier, quicker, more helpful, and less robotic and scripted. Contextual data helps users complete tasks faster and allows you to create more natural, human-like back and forth conversations.

For example, let us consider the following conversation:
User: What are the annual charges for a credit card?
Bot: First year is free and after that, it's $xxx
User: Sounds great, I would like to apply for one.

In the above conversation, the apply is in the context of a credit card. The bot should not be asking the user whether they would like to apply for a credit card or a debit card. The context from the previous intent, FAQ – credit card annual charges, should be passed to the intent, Apply for a card.

Kore.ai Bots Platform allows you to capture and reuse contextual data for a large variety of scenarios, so you can create more complex use cases and redefine the enterprise customer experience. The following are examples of a few such scenarios:

  • Sharing context across intents, FAQs: As seen from the above example, maintaining context for all intents i.e. dialog tasks, FAQs, makes it easy to customize the user experience
  • Context-driven FAQs: Certain intents (tasks or FAQs) can be made available only when certain other intents (tasks or FAQs) are in the context.
    For example, FAQ intent What are the meal options available? should be available only when Book a flight task is in the context.
  • Follow-up Intent: Context of the current intent can be used to identify subsequent intents from the user utterances.
    For example, User utterance what are the charges? should be responded with FAQ intent what are the charges for a Platinum credit card? if the user’s previous intent was what are the benefits of Platinum credit card?
  • Sharing Entity Values across Intents: Entity values or conversation flows can be driven using the previous intent’s context information.
    For example, The City Name entity in Check Weather intent can be pre-populated if the user has executed Check Flight Status intent and has provided value for the Destination City entity.

This document talks about the concepts behind the implementation of context management in the Kore.ai bots platform. For a detailed step-by-step example, refer here.

Use Case Example

User: When is my flight to Singapore?
Bot: Your flight from New York to Singapore is confirmed for Jun 20th.
User: Do I need a Visa?
Bot: Yes, you need a visa to visit Singapore for business or tourism
User: I would like to apply for one
Bot: Sure I can help with Visa to Singapore. Let me know the duration of the stay

To achieve the above conversation, the context object can be used as follows:

  • Flight Booking Enquiry emits the destination city entity value.
  • Visa FAQ uses the entity value emitted by the Booking Enquiry Intent.
  • Visa Application consumes the terms Visa and Singapore from the Visa FAQ.

This article will help you in achieving the above scenario.

Implementation

Context management involves the following steps:

  • Output Context to define tags that indicate the current intent is executed.
  • Intent Preconditions to extract the output context tags for scoping the subsequent intent detection.
  • Intent Detection Rules for detecting the relevant intents.
  • KC – Contextual Intent Detection using context tags to identify the terms/nodes from the FAQs.
  • Conversation Flows to customize the flows.

Output Context

Context Tags are generated and stored in the context object to be used for managing the bot behavior and user experience. The platform creates a context object for every user intent, like dialog tasks and FAQs (refer here for more on Context Object).

Default Contexts Tags
Intent names, Entity names, and FAQ Term/Node names are emitted by default.

Custom Context Tags
Additionally, the following can be defined to be included in the Context Object:

  • Context Tags – You can add context tags from the NLP settings for Dialog, Action, Alert, Info Tasks, and Entities.
  • Entity Value – You set an option to indicate whether entity values captured should be emitted or not for each entity node.
  • Use context tags for finding FAQ – You can indicate whether KG paths to be shortlisted using context tags.

Dialog Tasks

The platform supports emitting details of a dialog task when executed by the user:

  • The intent name is emitted as a contextual tag for all dialog tasks when the task execution is initiated.
  • You can add any additional tags from the NLP Properties tab of the dialog task (you may have to scroll down).
  • You can emit output context tags from any place where JavaScript is written (script node, advanced prompts, run a script option, etc.) using the contextTags.add(string value).

Alert Tasks

The platform supports emitting details of the alert tasks when executed by the user:

  • The task name is emitted as a contextual tag for all alert tasks when the task execution is initiated.
  • You can add any additional tags at the time of task creation under More Options or from the General Settings.
  • You can also emit output context tags from pre-processors or post-processors using the contextTags.add(string value).

Entity

Entity values captured from end-users are emitted based on the:

  • Auto emit the entity values captured switch. Entity Value Tags will be emitted as shown in the following section.
  • You have the option to add any additional tags.

Knowledge Graph

  • Node/term name is emitted as a contextual tag for all mandatory and optional terms present in the path qualified when a question is answered.
  • You can add any additional tags per term from the Settings page for the term/node.
  • You can also emit output context tags from advanced prompts using the contextTags.add(string value).

Intent Preconditions

Intent pre-conditions are used to define the intent detection scope for intents and FAQs. These are a set of conditions that must be fulfilled for the intent/FAQ to be detected and executed.

Dialog Tasks

Intent pre-conditions for dialog intents are set to define when a dialog is detected i.e. making a dialog available for detection only when specific tags are available in the context.

  1. You can add one or more intent pre-conditions for making a dialog intent available.
  2. Dialog intents with pre-conditions are detected only if the defined pre-conditions are met.
  3. The intent with a pre-condition set is treated as a sub-intent and will be part of the Linked Task Exception behavior from the Dialog level Hold and Resume settings.

Alert Tasks

Intent pre-conditions for alert tasks are set to define when a task is detected i.e. making a task available for detection only when specific tags are available in the context.

  1. You can add one or more intent pre-conditions for making a task intent available.
  2. Task intents with pre-conditions must be detected only if the defined pre-conditions are met.

Knowledge Graph

Intent pre-conditions for Knowledge Graph can be associated with terms.

  1. You can define intent pre-conditions for any of the terms present in the Knowledge Graph.
  2. Paths that contain terms with pre-conditions are qualified only if the pre-conditions are met.

Intent Detection

Contextual intent detection helps in detecting relevant intents using the output context set by previously executed intents.

Tasks

You can define Rules for identifying contextually relevant intents by using output context tags the same as traits (refer here for more).

Knowledge Graph

The platform consumes the output context tags and uses them for improving intent detection in the Knowledge Graph engine based on the flag set by the developer. This flag ensures that the context tags are used to qualify paths in Knowledge Graph. Context tags are used to extract terms and these terms are clubbed with any other terms present in user utterance. The consolidated list of terms is used for qualifying the path.

You can set this configuration by:

  1. Select the Build tab from the top menu
  2. From the left menu, click Natural Language > Thresholds & Configurations.
  3. Click Knowledge Graph.
  4. Locate Qualify Contextual Paths and set it to Yes.

Conversation Flows

The context tags available in the context object are used to customize the conversation flows.

These can be used:

  • To pre-populate entity values.
  • To define transition conditions.
  • For custom conversation flow.

Script to access the context tags are:

  • From the current context: context.currentTags.tags
  • From the previous context: context.historicTags[0].tags
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