GETTING STARTED
SearchAssist Overview
SearchAssist Introduction
Onboarding SearchAssist
Build your first App
Glossary
Release Notes
Current Version
Recent Updates
Previous Versions

CONCEPTS
Managing Sources
Introduction
Files
Web Pages
FAQs
Structured Data 
Connectors
Introduction to Connectors
SharePoint Connector
Confluence Cloud Connector
Zendesk Connector
ServiceNow Connector
Salesforce Connector
Azure Storage Connector
Google Drive Connector
Dropbox Connector
Oracle Knowledge Connector
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
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
Collaboration
Integrations
OpenAI Integration
Azure OpenAI Integration
Billing and Usage
Plan Details
Usage Logs
Order and Invoices

SearchAssist PUBLIC 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

Introduction to Workbench

Workbench is a SearchAssist tool that converts content into objectively indexed documents. It processes the ingested content in a series of stages known as Index Pipeline. Collectively, Index pipeline converts the ingested content into a document ready for indexing. Each stage performs a specific set of data transformations before passing the content onto the next stage in the pipeline. Each stage also has a stage-specific configuration. You can rearrange or sequence the stages in a preferred order on the basis of your business requirements. 

To configure the workbench and introduce Index Pipeline stages for content processing, go to the Workbench page under the Indices tab.

SearchAssist supports the following Index Pipeline stages. 

  • Field Mapping maps fields in an indexing pipeline document to a target field, sets values, copies values, removes fields, and more.
  • Entity Extraction uses NLP techniques to identify named entities from the source field.
  • Traits Extraction extracts specific attributes that search users might express in their conversations.
  • Custom Script stage allows you to enter customized scripts to perform any field mapping tasks like deleting or renaming fields.
  • Keyword Extraction automatically detects important words stored in a field.
  • Exclude Document stage drops all the documents that match the specified condition.
  • Semantic Meaning is a technique to understand the meaning and interpretation of words, signs, and sentence structure. This stage currently supports web page-related sources only.
  • Snippets Extraction helps you to identify relevant snippets from the ingested data.

SearchAssist allows you to develop a custom pipeline corresponding to an Index configuration to suit your business requirements.

Each indexing stage has properties like stage type, stage name, and applicable conditions to choose the documents that must be transformed and the change to be performed. Every time there is a change in any of the stages, train the system before testing to ensure that the latest configuration is being used for indexing. You can also test individual stages of the pipeline by temporarily making the other stages inactive.  

Introduction to Workbench

Workbench is a SearchAssist tool that converts content into objectively indexed documents. It processes the ingested content in a series of stages known as Index Pipeline. Collectively, Index pipeline converts the ingested content into a document ready for indexing. Each stage performs a specific set of data transformations before passing the content onto the next stage in the pipeline. Each stage also has a stage-specific configuration. You can rearrange or sequence the stages in a preferred order on the basis of your business requirements. 

To configure the workbench and introduce Index Pipeline stages for content processing, go to the Workbench page under the Indices tab.

SearchAssist supports the following Index Pipeline stages. 

  • Field Mapping maps fields in an indexing pipeline document to a target field, sets values, copies values, removes fields, and more.
  • Entity Extraction uses NLP techniques to identify named entities from the source field.
  • Traits Extraction extracts specific attributes that search users might express in their conversations.
  • Custom Script stage allows you to enter customized scripts to perform any field mapping tasks like deleting or renaming fields.
  • Keyword Extraction automatically detects important words stored in a field.
  • Exclude Document stage drops all the documents that match the specified condition.
  • Semantic Meaning is a technique to understand the meaning and interpretation of words, signs, and sentence structure. This stage currently supports web page-related sources only.
  • Snippets Extraction helps you to identify relevant snippets from the ingested data.

SearchAssist allows you to develop a custom pipeline corresponding to an Index configuration to suit your business requirements.

Each indexing stage has properties like stage type, stage name, and applicable conditions to choose the documents that must be transformed and the change to be performed. Every time there is a change in any of the stages, train the system before testing to ensure that the latest configuration is being used for indexing. You can also test individual stages of the pipeline by temporarily making the other stages inactive.