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
SharePoint Connector
Confluence Cloud Connector
Confluence Server Connector
Zendesk Connector
ServiceNow Connector
Salesforce Connector
Azure Storage Connector
Google Drive Connector
Dropbox Connector
Oracle Knowledge Connector
DotCMS 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

Troubleshooting Answers

Troubleshooting Answer Snippets

This guide provides you with frequently seen issues and general guidelines on troubleshooting the Answers feature in a SearchAssist application when you do not receive any answer in response or receive an incorrect answer where the response does not match the expected answer. 

Note that values for specific configurations discussed below may vary depending on the application’s requirements, the format of the ingested data, and other unique application characteristics. Understanding the application’s architecture and requirements is essential to ensure precise answers. 

CASE 1: No Answers are received as a response

When you do not see any answers in the response, follow the steps listed below to troubleshoot the setup. 


  • Verify LLM Integration. 
      1. Check for any connectivity issues or credential errors, ensuring that the necessary connections are properly established.
  • Verify the Answer Models enabled
      1. Make sure that Answer Snippets are enabled in the application settings.
      2. Ensure that the configurations for the answer snippets for enabled models are set correctly. 
  • Verify chunks 
      1. Use the chunk browser to check if chunks are generated from relevant ingested data. In case the chunks are not generated, train the application. When data is synced from connectors at regular intervals, or a web crawl is scheduled at regular intervals, there may be data that the application is not trained with. In such cases, training the application will create chunks from the latest data as well. 
  • Verify Prompt
    1. Ensure that the Prompt is correctly set and directs LLM to return the expected response.

CASE 2: Incorrect Answer received as a response

When you receive answers in response to your queries but the answers are not as expected, follow the steps listed below to troubleshoot the issue.

  • Verify Chunks
    1. Go to the Chunk Browser and examine the extracted content from the ingested data. Verify that the content is being accurately extracted and check if the relevant information you are searching for is included in the extracted content. In case the chunks from a specific source data are missing, train the application again. 
    2. Next, use the Debug tool to investigate the chunks being sent to LLM. If no chunks are qualifying, consider adjusting the similarity threshold to include more relevant content and repeat the test query.
  • Adjust Answer Model properties 
    1. Verify the properties of the answer model that is enabled are set correctly, like the similarity score, weights, etc. Understand the config parameters and experiment with their values to get the desired response. 
    2. Review the prompt for any possible errors or misunderstandings in interpretation, and try editing the prompt. Write clear and concise instructions in the prompt. Adhere to the rules of defining a prompt. 
  • Fine Tune Properties
    1. Use Beta features to experiment with chunk size and chunk overlap. 
    2. Customize the response length using beta features to limit the number of tokens in the answer. 
    3. Optimize embeddings by selecting vector fields and enabling vector search.
               Note that some of these settings may depend on the LLM models and can affect the overall LLM costs.  
  • Verify Business Rules
    1. Ensure that the business rules are modifying the results as expected. Business rules can modify outcomes by filtering, boosting, or hiding specific documents or chunks. 

Troubleshooting Answers

Troubleshooting Answer Snippets

This guide provides you with frequently seen issues and general guidelines on troubleshooting the Answers feature in a SearchAssist application when you do not receive any answer in response or receive an incorrect answer where the response does not match the expected answer. 

Note that values for specific configurations discussed below may vary depending on the application’s requirements, the format of the ingested data, and other unique application characteristics. Understanding the application’s architecture and requirements is essential to ensure precise answers. 

CASE 1: No Answers are received as a response

When you do not see any answers in the response, follow the steps listed below to troubleshoot the setup. 


  • Verify LLM Integration. 
      1. Check for any connectivity issues or credential errors, ensuring that the necessary connections are properly established.
  • Verify the Answer Models enabled
      1. Make sure that Answer Snippets are enabled in the application settings.
      2. Ensure that the configurations for the answer snippets for enabled models are set correctly. 
  • Verify chunks 
      1. Use the chunk browser to check if chunks are generated from relevant ingested data. In case the chunks are not generated, train the application. When data is synced from connectors at regular intervals, or a web crawl is scheduled at regular intervals, there may be data that the application is not trained with. In such cases, training the application will create chunks from the latest data as well. 
  • Verify Prompt
    1. Ensure that the Prompt is correctly set and directs LLM to return the expected response.

CASE 2: Incorrect Answer received as a response

When you receive answers in response to your queries but the answers are not as expected, follow the steps listed below to troubleshoot the issue.

  • Verify Chunks
    1. Go to the Chunk Browser and examine the extracted content from the ingested data. Verify that the content is being accurately extracted and check if the relevant information you are searching for is included in the extracted content. In case the chunks from a specific source data are missing, train the application again. 
    2. Next, use the Debug tool to investigate the chunks being sent to LLM. If no chunks are qualifying, consider adjusting the similarity threshold to include more relevant content and repeat the test query.
  • Adjust Answer Model properties 
    1. Verify the properties of the answer model that is enabled are set correctly, like the similarity score, weights, etc. Understand the config parameters and experiment with their values to get the desired response. 
    2. Review the prompt for any possible errors or misunderstandings in interpretation, and try editing the prompt. Write clear and concise instructions in the prompt. Adhere to the rules of defining a prompt. 
  • Fine Tune Properties
    1. Use Beta features to experiment with chunk size and chunk overlap. 
    2. Customize the response length using beta features to limit the number of tokens in the answer. 
    3. Optimize embeddings by selecting vector fields and enabling vector search.
               Note that some of these settings may depend on the LLM models and can affect the overall LLM costs.  
  • Verify Business Rules
    1. Ensure that the business rules are modifying the results as expected. Business rules can modify outcomes by filtering, boosting, or hiding specific documents or chunks.