GETTING STARTED
Kore.ai XO Platform
Virtual Assistants Overview
Natural Language Processing (NLP)
Concepts and Terminology
Quick Start Guide
Accessing the Platform
Navigating the Kore.ai XO Platform
Building a Virtual Assistant
Help & Learning Resources
Release Notes
Current Version
Recent Updates
Previous Versions
Deprecations
Request a Feature
CONCEPTS
Design
Storyboard
Overview
FAQs
Conversation Designer
Overview
Dialog Tasks
Mock Scenes
Dialog Tasks
Overview
Navigate Dialog Tasks
Build Dialog Tasks
Node Types
Overview
Intent Node
Dialog Node
Dynamic Intent Node
GenAI Node
GenAI Prompt
Entity Node
Form Node
Confirmation Node
Message Nodes
Logic Node
Bot Action Node
Service Node
Webhook Node
Script Node
Process Node
Agent Transfer
Node Connections
Node Connections Setup
Sub-Intent Scoping
Entity Types
Entity Rules
User Prompts or Messages
Voice Call Properties
Knowledge AI
Introduction
Knowledge Graph
Introduction
Terminology
Build a Knowledge Graph
Manage FAQs
Knowledge Extraction
Import or Export Knowledge Graph
Prepare Data for Import
Importing Knowledge Graph
Exporting Knowledge Graph
Auto-Generate Knowledge Graph
Knowledge Graph Analysis
Answer from Documents
Alert Tasks
Small Talk
Digital Skills
Overview
Digital Forms
Digital Views
Introduction
Widgets
Panels
Session and Context Variables
Context Object
Intent Discovery
Train
NLP Optimization
ML Engine
Overview
Model Validation
FM Engine
KG Engine
Traits Engine
Ranking and Resolver
Training Validations
NLP Configurations
NLP Guidelines
LLM and Generative AI
Introduction
LLM Integration
Prompts & Requests Library
Co-Pilot Features
Dynamic Conversations Features
Kore.ai XO GPT Model
Intelligence
Introduction
Event Handlers
Contextual Memory
Contextual Intents
Interruption Management
Multi-intent Detection
Amending Entities
Default Conversations
Conversation Driven Dialog Builder
Sentinment Management
Tone Analysis
Default Standard Responses
Ignore Words & Field Memory
Test & Debug
Overview
Talk to Bot
Utterance Testing
Batch Testing
Conversation Testing
Conversation Testing Overview
Create a Test Suite
Test Editor
Test Case Assertion
Test Case Execution Summary
Glossary
Health and Monitoring
NLP Health
Flow Health
Integrations
Actions
Actions Overview
Asana
Configure
Templates
Azure OpenAI
Configure
Templates
BambooHR
Configure
Templates
Bitly
Configure
Templates
Confluence
Configure
Templates
DHL
Configure
Templates
Freshdesk
Configure
Templates
Freshservice
Configure
Templates
Google Maps
Configure
Templates
Here
Configure
Templates
HubSpot
Configure
Templates
JIRA
Configure
Templates
Microsoft Graph
Configure
Templates
Open AI
Configure
Templates
Salesforce
Configure
Templates
ServiceNow
Configure
Templates
Stripe
Configure
Templates
Shopify
Configure
Templates
Twilio
Configure
Templates
Zendesk
Configure
Templates
Agents
Agent Transfer Overview
Custom (BotKit)
Drift
Genesys
Intercom
NiceInContact
NiceInContact(User Hub)
Salesforce
ServiceNow
Configure Tokyo and Lower versions
Configure Utah and Higher versions
Unblu
External NLU Adapters
Overview
Dialogflow Engine
Test and Debug
Deploy
Channels
Publishing
Versioning
Analyze
Introduction
Dashboard Filters
Overview Dashboard
Conversations Dashboard
Users Dashboard
Performance Dashboard
Custom Dashboards
Introduction
Custom Meta Tags
Create Custom Dashboard
Create Custom Dashboard Filters
NLP Insights
Task Execution Logs
Conversations History
Conversation Flows
Conversation Insights
Feedback Analytics
Usage Metrics
Containment Metrics
Universal Bots
Introduction
Universal Bot Definition
Universal Bot Creation
Training a Universal Bot
Universal Bot Customizations
Enabling Languages
Store
Manage Assistant
Team Collaboration
Plan & Usage
Overview
Usage Plans
Templates
Support Plans
Invoices
Authorization
Conversation Sessions
Multilingual Virtual Assistants
Get Started
Supported Components & Features
Manage Languages
Manage Translation Services
Multiingual Virtual Assistant Behavior
Feedback Survey
Masking PII Details
Variables
Collections
IVR Settings
General Settings
Assistant Management
Manage Namespace
Data
Overview
Data Table
Table Views
App Definitions
Data as Service
HOW TOs
Build a Travel Planning Assistant
Travel Assistant Overview
Create a Travel Virtual Assistant
Design Conversation Skills
Create an ‘Update Booking’ Task
Create a Change Flight Task
Build a Knowledge Graph
Schedule a Smart Alert
Design Digital Skills
Configure Digital Forms
Configure Digital Views
Train the Assistant
Use Traits
Use Patterns
Manage Context Switching
Deploy the Assistant
Use Bot Functions
Use Content Variables
Use Global Variables
Use Web SDK
Build a Banking Assistant
Design Conversation Skills
Create a Sample Banking Assistant
Create a Transfer Funds Task
Create a Update Balance Task
Create a Knowledge Graph
Set Up a Smart Alert
Design Digital Skills
Configure Digital Forms
Configure Digital Views
Add Data to Data Tables
Update Data in Data Tables
Add Data from Digital Forms
Train the Assistant
Composite Entities
Use Traits
Use Patterns for Intents & Entities
Manage Context Switching
Deploy the Assistant
Configure an Agent Transfer
Use Assistant Functions
Use Content Variables
Use Global Variables
Intent Scoping using Group Node
Analyze the Assistant
Create a Custom Dashboard
Use Custom Meta Tags in Filters
Migrate External Bots
Google Dialogflow Bot
APIs & SDKs
API Reference
API Introduction
Rate Limits
API List
koreUtil Libraries
SDK Reference
SDK Introduction
SDK Security
SDK Registration
Web Socket Connect and RTM
Installing the BotKit SDK
Using the BotKit SDK
SDK Events
SDK Functions
SDK Tutorials
BotKit - Blue Prism
BotKit - Flight Search Sample VA
BotKit - Agent Transfer
Widget SDK Tutorial
Web SDK Tutorial
ADMINISTRATION
Introduction to Admin Console
Administration Dashboard
User Management
Add Users
Manage Groups
Manage Roles
Data Tables and Views
Assistant Management
Enrollment
Invite Users
Send Bulk Invites
Import User Data
Synchronize Users from AD
Security & Control
Using Single-Sign On (SSO)
Two-Factor Authentication (2FA)
Security Settings
Cloud Connector
Analytics
Billing
  1. Home
  2. Docs
  3. Virtual Assistants
  4. Builder
  5. Dialog Task
  6. Data as Service

Data as Service

Kore.ai’s Data offering lets you define Data Tables, Table Views, and manipulate them from your Virtual Assistant. You can also view the definition of the data table or table view assigned to the VA and provide correct data mappings based on the data types for the table columns.

This section deals with the manipulation aspect using the Service Node within a Dialog Task of your Virtual Assistant. For details on the data definition, refer here.

Service Call – Table

From the assigned bots for any given data table/table view, you can query and manipulate the data using the Service Node in the Dialog Tasks.

Steps to configure the Service node:

  1. Open the Bot and the Dialog Task where you want to access the data.
  2. Add a Service Node at the appropriate location in the process flow.
  3. Click here for details on Service Node. Here we will be listing the configurations for Data Table integration.
  4. Component Properties configuration
    • General Settings section
      • Name – enter a name for the node;
      • Display Name – enter a display name for the node;
      • Service Type – choose Data Service;
      • Type – select Table.

Request Definition – define the service request by clicking the Define Request link. In the slide-out page configure the following:

  • Choose a Data Table – You can choose from the list, these are the data tables assigned to this bot.
  • Actions – Select the action you would like to perform:

  • You can Test the service request. Remember testing with context reference will fail as they will be evaluated at run time and the data will not be available till then.
  • Save the service request definition
  • You can set the Instance and Connections properties as per your bot requirements.
  • The data returned from the data table can be accessed from the context object and used in your task as per your need.

Add Data

To add data, you need to:

  • provide values for each column in the data table;
  • these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}
  • The response from this call would be:
      "stringTable": {
        "response": {
          "body": {
            "CustId": 1,
            "Type": "Preferred",
            "Address": "New York",
            "CustomerName": "John Smith",
            "Updated_On": 1593687904111,
            "Created_On": 1593687904111,
            "Updated_By": "st-b1376ff2-1111-1111-aa34-973ef73212f5",
            "Created_By": "st-b1376ff2-1111-1111-aa34-973ef73212f5",
            "sys_Id": "sys-5c46e351-ee51-5c27-80cf-c6c1e8f8f066",
            "_id": "5efdbf602de5bb5f3f54f728"
          }
        }
      }

Get Data

To filter and fetch data from the table, you can:

  • Choose Filters to define filter criteria using:
    • the column names – pick from the drop-down list;
    • an operator – choose from the list;
    • comparison value – these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}.
    • Multiple filter criteria can be defined using AND/OR connectors.
    • In the absence of filter criteria, all rows are fetched limited by the limit and offset values mentioned below.
  • Limit property can be used to set a limit on the number of records to be fetched. If not specified then 10 records would be fetched by default;
  • Offset property can be used to specify the records to be skipped from the result data set.
  • Data values can be accessed using: {{context.<service_node_name>.response.body.queryResult[<index>].<column_name>}}
  • The response from this service call would be:
    "customerdetails": {
        "response": {
          "body": {
    	"hasMore": true,
    	"total": 4,
    	"metaInfo": [
              {
                "name": "City",
                "type": "string"
              },
              {
                "name": "Name",
                "type": "string"
              },
              {
                "name": "sys_Id",
                "type": "string"
              },
              {
                "name": "Created_On",
                "type": "date"
              },
              {
                "name": "Updated_On",
                "type": "date"
              },
              {
                "name": "Created_By",
                "type": "string"
              },
              {
                "name": "Updated_By",
                "type": "string"
              }
            ],
            "queryResult": [
              {
                "CustId": 1,
                "Type": "Preferred",
                "Address": "New York",
                "CustomerName": "John Smith",
                "sys_Id": "sys-b088ab59-7640-5a8f-8999-61a265dd2bee",
                "Created_On": 1593152119161,
                "Updated_On": 1593152119161,
                "Created_By": "st-b1376ff2-2384-5541-aa34-973ef73212f5",
                "Updated_By": "st-b1376ff2-2384-5541-aa34-973ef73212f5"
              },
              {
                "CustId": 2,
                "Type": "Privileged",
                "Address": "Chicago",
                "CustomerName": "Jane Doe",
                "sys_Id": "sys-632c69ef-f6dd-5d83-ab32-f7837c8b63f9",
                "Created_On": 1593152443035,
                "Updated_On": 1593152443035,
                "Created_By": "st-b1376ff2-2384-5541-aa34-973ef73212f5",
                "Updated_By": "st-b1376ff2-2384-5541-aa34-973ef73212f5"
              }
    	 ]
          }
        }
      }

Update Data

To modify existing you can:

  • Assign Values against each column to be updated.
    If you check a particular column for the update and leave the value field blank, then the corresponding column original values will NOT be retained but will be set as empty.
  • The columns which are not checked for updating will retain the original values.
  • These values can be static or a reference to a context object.
  • Choose Filters to define the filter criteria to specify the rows to be updated using
    • the column names,
    • an operator and
    • filter value – these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}.
    • Multiple filter criteria can be defined using AND/OR connectors.

  • The response from this service call would be:
      "customerdetails": {
        "response": {
          "body": {
            "records": []
          }
        }
      }

Delete data

To delete rows from the data table you need

  • to define filter criteria to specify the rows to be deleted using
    • the column names,
    • an operator and
    • filter value, these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}. Multiple filter criteria can be defined using AND/OR connectors.

  • The response from this service request would be:
      "customerdetails": {
        "response": {
          "body": {
            "nDeleted": 1
          }
        }
      }

Service Call – View

From the assigned bots for any given data table/table view, you can query and manipulate the data using the Service Node in the Dialog Tasks.

Steps to configure the Service node:

  1. Open the Bot and the Dialog Task where you want to access the data.
  2. Add a Service Node at the appropriate location in the process flow.
  3. Click here for details on Service Node. Here we will be listing the configurations for Data Table integration.
  4. Component Properties configuration
    • General Settings section
      • Name – enter a name for the node;
      • Display Name – enter a display name for the node;
      • Service Type – choose Data Service;
      • Type – select View.
    • Request Definition – define the service request by clicking the Define Request link.
    • In the slide-out page configure the following:
      • Choose a Table View – You can choose from the list, these are the table views assigned to this bot.
      • Filter the results – you can further define filter criteria using
        • the column names,
        • an operator and
        • filter value – these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}.
      • Multiple filter criteria can be defined using AND/OR connectors.
      • In the absence of filter criteria, all rows are fetched limited by the limit and offset values mentioned below.
    • You can set a limit on the number of records fetched and
    • You can choose to skip a few records from the result data set by specifying the offset value.
    • You can Test the service request. Remember testing with context reference will fail as they will be evaluated at run time and the data will not be available till then.
    • Save the service request definition
  5. You can set the Instance and Connections properties as per your bot requirements.
  6. The data returned from the data table can be accessed from the context object and used in your task as per your need, using: {{context.<service_node_name>.response.body.queryResult[<index>].<column_name>}}
  7. The response from this service call would be:
      "CustomerView": {
        "response": {
          "body": {
            "hasMore": true,
            "total": 4,
            "metaInfo": [
              {
                "name": "type",
                "type": "string"
              },
              {
                "name": "address",
                "type": "string"
              }
            ],
            "queryResult": [
              {
                "type": "Gold",
                "address": "New York"
              },
              {
                "type": "Gold",
                "address": "Chicago"
              },
              {
                "type": "Gold",
                "address": "Chicago"
              }
            ]
          }
        }
      }

Data as Service

Kore.ai’s Data offering lets you define Data Tables, Table Views, and manipulate them from your Virtual Assistant. You can also view the definition of the data table or table view assigned to the VA and provide correct data mappings based on the data types for the table columns.

This section deals with the manipulation aspect using the Service Node within a Dialog Task of your Virtual Assistant. For details on the data definition, refer here.

Service Call – Table

From the assigned bots for any given data table/table view, you can query and manipulate the data using the Service Node in the Dialog Tasks.

Steps to configure the Service node:

  1. Open the Bot and the Dialog Task where you want to access the data.
  2. Add a Service Node at the appropriate location in the process flow.
  3. Click here for details on Service Node. Here we will be listing the configurations for Data Table integration.
  4. Component Properties configuration
    • General Settings section
      • Name – enter a name for the node;
      • Display Name – enter a display name for the node;
      • Service Type – choose Data Service;
      • Type – select Table.

Request Definition – define the service request by clicking the Define Request link. In the slide-out page configure the following:

  • Choose a Data Table – You can choose from the list, these are the data tables assigned to this bot.
  • Actions – Select the action you would like to perform:

  • You can Test the service request. Remember testing with context reference will fail as they will be evaluated at run time and the data will not be available till then.
  • Save the service request definition
  • You can set the Instance and Connections properties as per your bot requirements.
  • The data returned from the data table can be accessed from the context object and used in your task as per your need.

Add Data

To add data, you need to:

  • provide values for each column in the data table;
  • these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}
  • The response from this call would be:
      "stringTable": {
        "response": {
          "body": {
            "CustId": 1,
            "Type": "Preferred",
            "Address": "New York",
            "CustomerName": "John Smith",
            "Updated_On": 1593687904111,
            "Created_On": 1593687904111,
            "Updated_By": "st-b1376ff2-1111-1111-aa34-973ef73212f5",
            "Created_By": "st-b1376ff2-1111-1111-aa34-973ef73212f5",
            "sys_Id": "sys-5c46e351-ee51-5c27-80cf-c6c1e8f8f066",
            "_id": "5efdbf602de5bb5f3f54f728"
          }
        }
      }

Get Data

To filter and fetch data from the table, you can:

  • Choose Filters to define filter criteria using:
    • the column names – pick from the drop-down list;
    • an operator – choose from the list;
    • comparison value – these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}.
    • Multiple filter criteria can be defined using AND/OR connectors.
    • In the absence of filter criteria, all rows are fetched limited by the limit and offset values mentioned below.
  • Limit property can be used to set a limit on the number of records to be fetched. If not specified then 10 records would be fetched by default;
  • Offset property can be used to specify the records to be skipped from the result data set.
  • Data values can be accessed using: {{context.<service_node_name>.response.body.queryResult[<index>].<column_name>}}
  • The response from this service call would be:
    "customerdetails": {
        "response": {
          "body": {
    	"hasMore": true,
    	"total": 4,
    	"metaInfo": [
              {
                "name": "City",
                "type": "string"
              },
              {
                "name": "Name",
                "type": "string"
              },
              {
                "name": "sys_Id",
                "type": "string"
              },
              {
                "name": "Created_On",
                "type": "date"
              },
              {
                "name": "Updated_On",
                "type": "date"
              },
              {
                "name": "Created_By",
                "type": "string"
              },
              {
                "name": "Updated_By",
                "type": "string"
              }
            ],
            "queryResult": [
              {
                "CustId": 1,
                "Type": "Preferred",
                "Address": "New York",
                "CustomerName": "John Smith",
                "sys_Id": "sys-b088ab59-7640-5a8f-8999-61a265dd2bee",
                "Created_On": 1593152119161,
                "Updated_On": 1593152119161,
                "Created_By": "st-b1376ff2-2384-5541-aa34-973ef73212f5",
                "Updated_By": "st-b1376ff2-2384-5541-aa34-973ef73212f5"
              },
              {
                "CustId": 2,
                "Type": "Privileged",
                "Address": "Chicago",
                "CustomerName": "Jane Doe",
                "sys_Id": "sys-632c69ef-f6dd-5d83-ab32-f7837c8b63f9",
                "Created_On": 1593152443035,
                "Updated_On": 1593152443035,
                "Created_By": "st-b1376ff2-2384-5541-aa34-973ef73212f5",
                "Updated_By": "st-b1376ff2-2384-5541-aa34-973ef73212f5"
              }
    	 ]
          }
        }
      }

Update Data

To modify existing you can:

  • Assign Values against each column to be updated.
    If you check a particular column for the update and leave the value field blank, then the corresponding column original values will NOT be retained but will be set as empty.
  • The columns which are not checked for updating will retain the original values.
  • These values can be static or a reference to a context object.
  • Choose Filters to define the filter criteria to specify the rows to be updated using
    • the column names,
    • an operator and
    • filter value – these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}.
    • Multiple filter criteria can be defined using AND/OR connectors.

  • The response from this service call would be:
      "customerdetails": {
        "response": {
          "body": {
            "records": []
          }
        }
      }

Delete data

To delete rows from the data table you need

  • to define filter criteria to specify the rows to be deleted using
    • the column names,
    • an operator and
    • filter value, these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}. Multiple filter criteria can be defined using AND/OR connectors.

  • The response from this service request would be:
      "customerdetails": {
        "response": {
          "body": {
            "nDeleted": 1
          }
        }
      }

Service Call – View

From the assigned bots for any given data table/table view, you can query and manipulate the data using the Service Node in the Dialog Tasks.

Steps to configure the Service node:

  1. Open the Bot and the Dialog Task where you want to access the data.
  2. Add a Service Node at the appropriate location in the process flow.
  3. Click here for details on Service Node. Here we will be listing the configurations for Data Table integration.
  4. Component Properties configuration
    • General Settings section
      • Name – enter a name for the node;
      • Display Name – enter a display name for the node;
      • Service Type – choose Data Service;
      • Type – select View.
    • Request Definition – define the service request by clicking the Define Request link.
    • In the slide-out page configure the following:
      • Choose a Table View – You can choose from the list, these are the table views assigned to this bot.
      • Filter the results – you can further define filter criteria using
        • the column names,
        • an operator and
        • filter value – these values can be static or a reference to a context object  for example {{context.entities.<entity-name>}}.
      • Multiple filter criteria can be defined using AND/OR connectors.
      • In the absence of filter criteria, all rows are fetched limited by the limit and offset values mentioned below.
    • You can set a limit on the number of records fetched and
    • You can choose to skip a few records from the result data set by specifying the offset value.
    • You can Test the service request. Remember testing with context reference will fail as they will be evaluated at run time and the data will not be available till then.
    • Save the service request definition
  5. You can set the Instance and Connections properties as per your bot requirements.
  6. The data returned from the data table can be accessed from the context object and used in your task as per your need, using: {{context.<service_node_name>.response.body.queryResult[<index>].<column_name>}}
  7. The response from this service call would be:
      "CustomerView": {
        "response": {
          "body": {
            "hasMore": true,
            "total": 4,
            "metaInfo": [
              {
                "name": "type",
                "type": "string"
              },
              {
                "name": "address",
                "type": "string"
              }
            ],
            "queryResult": [
              {
                "type": "Gold",
                "address": "New York"
              },
              {
                "type": "Gold",
                "address": "Chicago"
              },
              {
                "type": "Gold",
                "address": "Chicago"
              }
            ]
          }
        }
      }
Menu