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
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
Kore.ai XO GPT Module
Prompts & Requests Library
Co-Pilot Features
Dynamic Conversations Features
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
LLM and Generative AI Logs
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
Web SDK
How the Web SDK Works
SDK Security
SDK Registration
Web Socket Connect and RTM
Tutorials
Widget SDK Tutorial
Web SDK Tutorial
BotKit SDK
BotKit SDK Deployment Guide
Installing the BotKit SDK
Using the BotKit SDK
SDK Events
SDK Functions
Tutorials
BotKit - Blue Prism
BotKit - Flight Search Sample VA
BotKit - Agent Transfer
  1. Integrations
  2. Dialogflow Engine

Dialogflow Engine

The Dialogflow engine is responsible for understanding user inputs and providing relevant responses based on pre-defined intents and entities. It uses natural language processing (NLP) and machine learning algorithms to identify the user’s intent and extract relevant information from their inputs. It also enables developers to build complex conversational flows, manage context, and integrate with external APIs. To use the Dialogflow engine for Natural Language Understanding, you need to create the agent and train the model on Dialogflow if you don’t have one already. The following sections detail the agent creation and training steps and also explain how to obtain the information needed to configure the Dialogflow ES Adapter. Once all the configurations are done, you can start testing the conversation behavior/flow of the bot using your Dialogflow ES model.

Train the Dialogflow Engine

Dialogflow ES NLU is one of the external NLU adapters. If you want to configure the Dialogflow ES adapter, you need the Project ID and private key obtained from the JSON file in the google cloud console. See Enable the Dialogflow API and Create Service Account Credentials to understand how to obtain the Project ID and the Private Key. These sections explain training and usage of the Dialogflow NLU engine.

Prerequisites: You need to have at least one agent created on the Dialogflow Essentials page, to be able to configure the intent, entity nodes, or FAQs.

To enable intent and entity detection using the Dialogflow engine during a user conversation, you need to create the intents and entities on the Dialogflow Essentials cloud page with the same names used during their creation on Kore.ai XO Platform.

The following steps explain how to create an agent, intent and test the configuration.

  1. Go to Dialogflow Essentials and click Create Agent, if you don’t have any available agent. In this example an agent with the name Test_Dialogflow is created.
  2. Click Create Intent to create an intent with the same name as in your VA.
  3. In this example an intent with name Phone Number Verification is created as we have the same user intent in the VA.
  4. Under Action and Parameters, add Action as per the name of the entity. Add the Parameter Name, Entity, Value and the Prompt details.
  5. Under Responses → Default, add the Text Response to be displayed for the user.
  6. Click Save to save the configuration.
  7. Go to Training in the left side menu and enter the intent name at User Says.
  8. Enter the intent name same as it is in the VA, in the top most right corner of the page in Try Now, to test the Dialogflow configuration.
  9. The configured text response would be displayed under the Default Response.
  10. Note: You can configure any intents, entities or FAQ details which you require to be identified using the Dialogflow ES adapter.

Enable the Dialogflow API

You can activate the Dialogflow API feature for your agent within Dialogflow cloud page.
Follow these steps:

  1. Click the Gear icon next to the agent name from the top left of the Dialogflow dashboard.
  2. The General Settings page is displayed. From there scroll down, to note the project ID value.
  3. Note: The Project ID will be used to fill the credentials while enabling the Dialogflow adapter on the Kore.AI platform.
  4. Click on the project ID to open google cloud.
  5. You will navigate to the Google Cloud dashboard.
  6. Select APIs & Services → Enabled APIs & Services from the left resources menu.
  7. Click + Enable APIs and Services from the top of the display and find the Dialogflow API option.

  8. Upon activation of Dialogflow API, select Manage to open the Dialogflow API management dashboard.

Create Service Account Credentials

The next step on the Dialogflow API management dashboard is to create a set of credentials to use the API, and to connect to the Dialogflow cloud from the Kore.ai platform.

Service Account Creation

Follow these steps to create service account:

  1. Click the button Create Credentials at the top of the screen.
  2. Note: If you already have service accounts with credentials, you can access them from the Credentials option in the left navigation menu.

  3. In the Create Credentials page, the Dialogflow API is by default selected in the Select an API drop-down.
  4. Select Application Data option for What data will you be accessing? as you are creating a service account. Click Next.
  5. Select No. I’m not using them and click Next.
  6. In the Service account details page, provide details for service account name, ID and description as shown below.
  7. Click Create and Continue. The service account is created.
  8. Grant access permissions to this service account and also grant users access to this service account if required in the next pages that get displayed.

Private Key Generation

Follow these steps to generate the private key:

  1. Click on the service account created under Service Accounts in the API & Services page as shown below.
  2. The service account details are displayed as shown in the following screenshot. Go to the Keys tab.
  3. Select Create a new key in the Add Key drop-down to generate the private key.
  4. In the displayed pop-up, select JSON as the option to generate the private key.

Note: The JSON option is by default selected.

A file containing the JSON Private Key information for use in the External NLU settings of Kore.ai platform, is downloaded and saved to your computer.

The file has the layout as follows:
{
"type": "***",
"project_id": "***",
"private_key_id": "***",
"private_key": "***",
"client_email": "***",
"client_id": "***",
"auth_uri": "***",
"token_uri": "***",
"auth_provider_x509_cert_url": "***",
"client_x509_cert_url": "***"
}

Note: Save this JSON payload securely. You will not be able to access it again. The entire content of this JSON file must be copied into the private key field inside the Kore.AI External NLU adapter settings. See Adapter Configuration to know more.

To understand the testing and debugging of your VA, to validate the working of the DialogFlow NLU, see Test and Debug.

Dialogflow Engine

The Dialogflow engine is responsible for understanding user inputs and providing relevant responses based on pre-defined intents and entities. It uses natural language processing (NLP) and machine learning algorithms to identify the user’s intent and extract relevant information from their inputs. It also enables developers to build complex conversational flows, manage context, and integrate with external APIs. To use the Dialogflow engine for Natural Language Understanding, you need to create the agent and train the model on Dialogflow if you don’t have one already. The following sections detail the agent creation and training steps and also explain how to obtain the information needed to configure the Dialogflow ES Adapter. Once all the configurations are done, you can start testing the conversation behavior/flow of the bot using your Dialogflow ES model.

Train the Dialogflow Engine

Dialogflow ES NLU is one of the external NLU adapters. If you want to configure the Dialogflow ES adapter, you need the Project ID and private key obtained from the JSON file in the google cloud console. See Enable the Dialogflow API and Create Service Account Credentials to understand how to obtain the Project ID and the Private Key. These sections explain training and usage of the Dialogflow NLU engine.

Prerequisites: You need to have at least one agent created on the Dialogflow Essentials page, to be able to configure the intent, entity nodes, or FAQs.

To enable intent and entity detection using the Dialogflow engine during a user conversation, you need to create the intents and entities on the Dialogflow Essentials cloud page with the same names used during their creation on Kore.ai XO Platform.

The following steps explain how to create an agent, intent and test the configuration.

  1. Go to Dialogflow Essentials and click Create Agent, if you don’t have any available agent. In this example an agent with the name Test_Dialogflow is created.
  2. Click Create Intent to create an intent with the same name as in your VA.
  3. In this example an intent with name Phone Number Verification is created as we have the same user intent in the VA.
  4. Under Action and Parameters, add Action as per the name of the entity. Add the Parameter Name, Entity, Value and the Prompt details.
  5. Under Responses → Default, add the Text Response to be displayed for the user.
  6. Click Save to save the configuration.
  7. Go to Training in the left side menu and enter the intent name at User Says.
  8. Enter the intent name same as it is in the VA, in the top most right corner of the page in Try Now, to test the Dialogflow configuration.
  9. The configured text response would be displayed under the Default Response.
  10. Note: You can configure any intents, entities or FAQ details which you require to be identified using the Dialogflow ES adapter.

Enable the Dialogflow API

You can activate the Dialogflow API feature for your agent within Dialogflow cloud page.
Follow these steps:

  1. Click the Gear icon next to the agent name from the top left of the Dialogflow dashboard.
  2. The General Settings page is displayed. From there scroll down, to note the project ID value.
  3. Note: The Project ID will be used to fill the credentials while enabling the Dialogflow adapter on the Kore.AI platform.
  4. Click on the project ID to open google cloud.
  5. You will navigate to the Google Cloud dashboard.
  6. Select APIs & Services → Enabled APIs & Services from the left resources menu.
  7. Click + Enable APIs and Services from the top of the display and find the Dialogflow API option.

  8. Upon activation of Dialogflow API, select Manage to open the Dialogflow API management dashboard.

Create Service Account Credentials

The next step on the Dialogflow API management dashboard is to create a set of credentials to use the API, and to connect to the Dialogflow cloud from the Kore.ai platform.

Service Account Creation

Follow these steps to create service account:

  1. Click the button Create Credentials at the top of the screen.
  2. Note: If you already have service accounts with credentials, you can access them from the Credentials option in the left navigation menu.

  3. In the Create Credentials page, the Dialogflow API is by default selected in the Select an API drop-down.
  4. Select Application Data option for What data will you be accessing? as you are creating a service account. Click Next.
  5. Select No. I’m not using them and click Next.
  6. In the Service account details page, provide details for service account name, ID and description as shown below.
  7. Click Create and Continue. The service account is created.
  8. Grant access permissions to this service account and also grant users access to this service account if required in the next pages that get displayed.

Private Key Generation

Follow these steps to generate the private key:

  1. Click on the service account created under Service Accounts in the API & Services page as shown below.
  2. The service account details are displayed as shown in the following screenshot. Go to the Keys tab.
  3. Select Create a new key in the Add Key drop-down to generate the private key.
  4. In the displayed pop-up, select JSON as the option to generate the private key.

Note: The JSON option is by default selected.

A file containing the JSON Private Key information for use in the External NLU settings of Kore.ai platform, is downloaded and saved to your computer.

The file has the layout as follows:
{
"type": "***",
"project_id": "***",
"private_key_id": "***",
"private_key": "***",
"client_email": "***",
"client_id": "***",
"auth_uri": "***",
"token_uri": "***",
"auth_provider_x509_cert_url": "***",
"client_x509_cert_url": "***"
}

Note: Save this JSON payload securely. You will not be able to access it again. The entire content of this JSON file must be copied into the private key field inside the Kore.AI External NLU adapter settings. See Adapter Configuration to know more.

To understand the testing and debugging of your VA, to validate the working of the DialogFlow NLU, see Test and Debug.

메뉴