GETTING STARTED
SearchAssist Overview
SearchAssist Introduction
Onboarding SearchAssist
Build your first App
Glossary
Release Notes
What's new in SearchAssist
Previous Versions

CONCEPTS
Managing Sources
Introduction
Files
Web Pages
FAQs
Structured Data 
Connectors
Introduction to Connectors
Azure Storage Connector
Confluence Cloud Connector
Confluence Server Connector
Custom Connector
DotCMS Connector
Dropbox Connector
Google Drive Connector
Oracle Knowledge Connector
Salesforce Connector
ServiceNow Connector
SharePoint Connector
Zendesk Connector
RACL
Virtual Assistants
Managing Indices
Introduction
Index Fields
Traits
Workbench
Introduction to Workbench
Field Mapping
Entity Extraction
Traits Extraction
Keyword Extraction
Exclude Document
Semantic Meaning
Snippet Extraction
Custom LLM Prompts
Index Settings
Index Languages
Managing Chunks
Chunk Browser
Managing Relevance
Introduction
Weights
Highlighting
Presentable
Synonyms
Stop Words
Search Relevance
Spell Correction
Prefix Search
Custom Configurations
Personalizing Results
Introduction
Answer Snippets
Introduction
Extractive Model
Generative Model
Enabling Both Models
Simulation and Testing
Debugging
Best Practices and Points to Remember
Troubleshooting Answers
Answer Snippets Support Across Content Sources
Result Ranking
Facets
Business Rules
Introduction
Contextual Rules
NLP Rules
Engagement
Small Talk
Bot Actions
Designing Search Experience
Introduction
Search Interface
Result Templates
Testing
Preview and Test
Debug Tool
Running Experiments
Introduction
Experiments
Analyzing Search Performance
Overview
Dashboard
User Engagement
Search Insights
Result Insights
Answer Insights

ADMINISTRATION
General Settings
Credentials
Channels
Team
Collaboration
Integrations
OpenAI Integration
Azure OpenAI Integration
Custom Integration
Billing and Usage
Plan Details
Usage Logs
Order and Invoices
Smart Hibernation

SearchAssist APIs
API Introduction
API List

SearchAssist SDK

HOW TOs
Use Custom Fields to Filter Search Results and Answers
Add Custom Metadata to Ingested Content
Write Painless Scripts
Configure Business Rules for Generative Answers

Workbench

Workbench combines the key aspects of the data indexing configuration process for transforming the content from data sources into objects for indexing.
An Index Pipeline transforms the content into a document suitable for indexing. It consists of a series of configurable index pipeline stages. Each stage performs a different transformation on the data before passing the result to the next stage in the pipeline. For example, you can extract the entity values before processing trait properties.
SearchAssist provides a built-in pipeline that allows Index Workbench to develop custom index pipelines to suit any application. 

Index Pipeline Stages

The data transformation is done in a series of operations called stages. SearchAssist provides many specialized index stages and custom script stages that allow custom processing. Each stage has a stage-specific configuration. You can order the stages in your preferred flow.

A pipeline stage consists of properties like stage type, stage name, and condition. You can write conditions to choose the documents that must be transformed. For example, you can write a condition to select only FAQs.
Remember to Train your app each time you make changes to any index configuration. This would build the index based on the updated configurations.

In case you want to test the application for select stages and not all, you can choose to render a particular stage inactive. This will retain the stage but at the same time, it won’t be considered for index configuration. You can make the stages active at a later time as per your needs.

Index pipeline stages are listed below:

  • The Field Mapping stage is used to map fields in an index pipeline document to a target field, set values, copy values, remove fields, and much more. Refer here for details.
  • Keyword Extraction is a technique to automatically detect important words from the text stored in a field. Refer here for details.
  • Traits Extraction extracts specific entities, attributes, or details that the search users might express in their conversations. Refer here for details.
  • Entity Extraction uses NLP techniques to identify named entities from the source field. Refer here for details.
  • Semantic Meaning analysis is the technique to understand the meaning and interpretation of words, signs, and sentence structure. Refer here for details.
  • Custom Script stage allows you to enter customized scripts to perform any field mapping tasks like deleting or renaming fields. Refer here for details.
  • Exclude Document stage drops all the documents that match the specified condition. Refer here for details.

Simulate

The Workbench is bundled with a Simulator that provides an interactive preview of how stage rules affect a document before it is indexed.

You can simulate the index stage execution of sample documents using the Simulate button. The stages up to the one selected would be implemented. For example, if you have three stages Traits, Webdomain Keyword, and FAQ Keyword Extraction in that order and you hit Simulate in Webdomain keywords stage then the simulation would extract Traits and Webdomain keywords.

The simulated result highlights the impacted fields and allows you to see the fields available in the document.

Workbench

Workbench combines the key aspects of the data indexing configuration process for transforming the content from data sources into objects for indexing.
An Index Pipeline transforms the content into a document suitable for indexing. It consists of a series of configurable index pipeline stages. Each stage performs a different transformation on the data before passing the result to the next stage in the pipeline. For example, you can extract the entity values before processing trait properties.
SearchAssist provides a built-in pipeline that allows Index Workbench to develop custom index pipelines to suit any application. 

Index Pipeline Stages

The data transformation is done in a series of operations called stages. SearchAssist provides many specialized index stages and custom script stages that allow custom processing. Each stage has a stage-specific configuration. You can order the stages in your preferred flow.

A pipeline stage consists of properties like stage type, stage name, and condition. You can write conditions to choose the documents that must be transformed. For example, you can write a condition to select only FAQs.
Remember to Train your app each time you make changes to any index configuration. This would build the index based on the updated configurations.

In case you want to test the application for select stages and not all, you can choose to render a particular stage inactive. This will retain the stage but at the same time, it won’t be considered for index configuration. You can make the stages active at a later time as per your needs.

Index pipeline stages are listed below:

  • The Field Mapping stage is used to map fields in an index pipeline document to a target field, set values, copy values, remove fields, and much more. Refer here for details.
  • Keyword Extraction is a technique to automatically detect important words from the text stored in a field. Refer here for details.
  • Traits Extraction extracts specific entities, attributes, or details that the search users might express in their conversations. Refer here for details.
  • Entity Extraction uses NLP techniques to identify named entities from the source field. Refer here for details.
  • Semantic Meaning analysis is the technique to understand the meaning and interpretation of words, signs, and sentence structure. Refer here for details.
  • Custom Script stage allows you to enter customized scripts to perform any field mapping tasks like deleting or renaming fields. Refer here for details.
  • Exclude Document stage drops all the documents that match the specified condition. Refer here for details.

Simulate

The Workbench is bundled with a Simulator that provides an interactive preview of how stage rules affect a document before it is indexed.

You can simulate the index stage execution of sample documents using the Simulate button. The stages up to the one selected would be implemented. For example, if you have three stages Traits, Webdomain Keyword, and FAQ Keyword Extraction in that order and you hit Simulate in Webdomain keywords stage then the simulation would extract Traits and Webdomain keywords.

The simulated result highlights the impacted fields and allows you to see the fields available in the document.