Faq

Search AI Frequently Asked Questions

Overview

This document addresses common questions about application training, multilingual support, and user feedback handling in Search AI.

Application Training

Training prepares ingested content for search by applying configurations, extracting chunks, and generating embeddings. Training is required whenever content or configuration changes.

Training Types

Type	Description	Scope
Full Training	Complete training of the application	All content, regardless of changes
Incremental Training	Training only for changed content	Additions, deletions, or modifications

Automatic Training

The application automatically trains when new content is ingested through file uploads, web crawls, or connectors.

Scenario	Behavior
Initial Ingestion	Full training of all ingested content
Incremental Updates	Training only for content changes (recrawling, connector resync)

When auto-training initiates, a banner appears at the top of the application interface.

Manual Training

Use the Train button on the Extract or Vector Configuration page to force retraining.

When Manual Training is Required:

Scenario	Examples
Config Updates	Changes to extraction strategies, vector configuration
Content Deletion	Removing files or content sources

Training Scope Based on Change Type:

Change Location	Reprocessing Scope
Before extraction stage	Reprocesses from extraction and chunking onward (e.g., connector config, schema, extraction strategy)
After extraction stage	Skips re-chunking, updates enrichment and vector generation only (e.g., embedding model, embedding fields)

Training Logs

View detailed training logs with document-level visibility:

1. Navigate to the Extract page
2. Click the dropdown with the Train option
3. Select View Training Logs

Log Information:

Field	Description
Training Type	Full or Incremental
Trigger Time	When training was initiated
Successful Docs	Number of documents processed successfully
Failed Docs	Number of documents with errors
Overall Status	Training marked as failed if any doc fails

Click individual records to view details grouped by extraction strategy.

Important Notes

• Manual chunk edits are overwritten during retraining for affected content only
• Manually resynchronizing a connector may require manual training trigger (known issue)

Multilingual Support

Search AI supports multilingual capabilities, enabling users to interact in their preferred language.

Core Capabilities

Feature	Description
Content Management	Add and manage content in multiple languages
Query Processing	Submit queries in supported languages
Response Generation	Receive answers in the same language as the query

Language Support Requirements

Multilingual support works with any language supported by your configured LLM and vector generation model when using:

• Text Extraction strategy
• Vector Retrieval method

No additional configuration is required for basic multilingual support.

Widely Supported Languages

Search AI supports languages commonly handled by advanced LLMs and embedding models like BGE-M3. Refer to your LLM or vector generation model’s official documentation for a comprehensive list.

Language-Sensitive Components

Certain modules require specific strategies or models depending on the language:

Content Extraction:

Language	Supported Strategies
English	All extraction methods
Other languages	Varies by method - consult documentation

Vector Generation Models:

Model	Language Support
BGE-M3	Wide range of languages; performance may be lower for underrepresented languages
Other models	Varies - check model documentation

Re-Ranker Models:

Model	Best For
Cross Encoder (ms-marco-MiniLM)	English - lightweight and fast
BGE Re-Ranker (bge-reranker-v2-m3)	Multilingual - lightweight with broad language support
MixedBread Re-Ranker	Highest accuracy - resource intensive

Best Practices

1. Verify model compatibility - Ensure your LLM and embedding model support target languages
2. Test across languages - Validate answer quality in each supported language
3. Consider re-ranker selection - Choose based on primary language requirements
4. Monitor performance - Low-resource languages may have reduced accuracy

User Feedback Handling

The feedback mechanism allows end users to rate response quality, helping evaluate and improve answer delivery.

How Feedback Works

Users express satisfaction through thumbs up/down actions captured via:

• Web SDK
• Public API

Enabling Feedback

1. Navigate to Configuration > Answer Generation
2. Enable Feedback Configuration

Capturing Feedback

Via Web SDK:

When enabled, thumbs up/down icons appear with each answer in the SDK interface.

Via API:

Use the Feedback API to capture feedback programmatically.

Viewing Feedback Data

Feedback appears in Analytics > Search AI > Answer Insights.

Feedback Display:

Scenario	Display
Majority positive	Green indicator with positive count
Majority negative	Red indicator with negative count
Mixed feedback	Highlights majority sentiment

Example: 20 feedback entries with 16 positive and 4 negative displays as green with count of 16.

Detailed Feedback Analysis

1. Click a query in Answer Insights to view the Answer Summary page
2. See all answers users received for that query with associated feedback
3. Click View Details for any answer to see user comments

Implementation Notes

When using SearchAINode:

• Ensure searchRequestId is included in the channel response
• Automatic when SearchAINode response is presented directly
• Must be explicitly included if response is saved to context and rendered with a custom template

Feedback vs. Feedback Surveys

Feature	Purpose
Search AI Feedback	Captures answer relevance and accuracy ratings
Platform Feedback Surveys	General survey capabilities in AI for Service platform

These are separate mechanisms - the Search AI feedback mechanism is specifically designed for answer quality evaluation.

Quick Reference

Training Summary

Question	Answer
When does auto-training occur?	On content ingestion (uploads, crawls, connector syncs)
When is manual training needed?	Config changes, content deletion
Where to trigger manual training?	Extract or Vector Configuration page
Where to view training logs?	Extract page > Train dropdown > View Training Logs

Multilingual Summary

Question	Answer
What languages are supported?	Any language supported by configured LLM and embedding model
Is configuration required?	No additional setup for basic support
Which re-ranker for multilingual?	BGE Re-Ranker (bge-reranker-v2-m3)
Which re-ranker for English only?	Cross Encoder (ms-marco-MiniLM)

Feedback Summary

Question	Answer
How to enable feedback?	Configuration > Answer Generation > Enable Feedback Configuration
Where to view feedback?	Analytics > Search AI > Answer Insights
How to capture via API?	Use the Feedback API
What does feedback show?	Thumbs up/down counts with majority sentiment highlighted

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