---
sourceDocument: Brazil ServiceNow AI Platform Administration
sourceDocumentLink: https://www.servicenow.com/docs/r/platform-administration

 Release :

    - brazil

ft:locale :

    - en-US

ft:publication_title :

    - Brazil ServiceNow AI Platform Administration

ft:clusterId :

    - platadm

bundleId :

    - platadm

workflow :

    - Platform


---

# Semantic index configuration for indexed sources

# Semantic index configuration for indexed sources {#ariaid-title1}

Release version: Brazil  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read  
The AI Search generalized RAG (Retrieval-Augmented Generation) framework offers a streamlined way to configure semantic indexing settings for records indexed from ServiceNow AI Platform® tables.

## Semantic index configuration overview {#semantic-index-cfg-ais__section_qgb_kbh_xcc}

AI Search admins can configure semantic indexing settings for an indexed source. These settings specify how AI Search indexes content from the indexed source for use with semantic vector search. The group of semantic indexing settings for a particular indexed source is called a semantic index configuration.  
Each semantic index configuration includes the following elements:

Indexed source

:   A reference to an existing indexed source with field values or attachments that you want indexed for semantic vector search.

    For more information on indexed sources, see [Indexed sources in AI Search](https://www.servicenow.com/docs/wbgc0tkdQcXHU63sXMrFeA "Indexed sources designate ServiceNow AI Platform tables and external document sets with alphanumeric text and string field content that you want to make searchable. AI Search ingests text and string fields from table records or external documents and stores their searchable alphanumeric content in its search index."). To learn more about semantic vector search, see [Semantic vector search in AI Search](https://www.servicenow.com/docs/b9O9dp2gJ2UH0R3S56GYzA "Semantic vector search enables the Now LLM Service to find results based on how similar they are in meaning to your search terms. Knowledge base articles Genius Results and ServiceNow Otto for Virtual Agent use semantic vector search to improve recall with natural language interpretation of search queries.").

Embedding models

:   A list of one or more embedding models for the system to use when indexing content from the indexed source for semantic vector search.

    An embedding model specifies how information found in your indexed content is encoded in a vector map. Semantic vector search uses the encoded information from the vector map to find search results that have meanings
    similar to those of your search terms.

Chunking strategy and related parameters

:   A chunking strategy and related parameter values that together determine how content from the indexed source's selected fields and attachments is handled during indexing for semantic vector search.

    Chunking is the process of breaking text down into smaller portions (called chunks) during indexing. By chunking your content, AI Search reduces search load and improves context and relevancy for semantic vector matches.

    The following image shows how a two-paragraph block of sample field value text might be broken into chunks for semantic indexing. As shown, chunks can contain multiple sentences and may span paragraph breaks found in the
    original text.

Semantic index fields

:   References to one or more semantic index fields that provide semantic indexing settings for content from the indexed source.

    Each semantic index field defines semantic indexing settings for a single field from the indexed source table, or for attachments from that table. You can specify the order in which semantic index fields are evaluated when
indexing content from the indexed source for semantic vector search.  
Note:  
You can define multiple semantic index configurations for an indexed source, but each configuration after the first imposes an additional performance cost at indexing time.

## Activating semantic index configuration {#semantic-index-cfg-ais__section_tgb_lbh_xcc}

Semantic index configuration functionality is provided by the AI Search Semantic Controller plugin (com.glide.ais.semantic_search). This plugin is automatically activated for your instance when you install any [ServiceNow Otto® application](https://www.servicenow.com/docs/access?context=platform-now-assist-landing&version=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US).{#semantic-index-cfg-ais__ais-semantic-controller-plugin-id-ph}

You can verify whether semantic index configuration is activated by navigating to AllAI SearchAI Search IndexIndexed Sources and selecting an indexed source record. If you see the Semantic Index Configuration related list on the Indexed Source form, the plugin is activated.
* **[Configure semantic indexing settings for an indexed source](https://www.servicenow.com/docs/d_wOxuJAVE0zNW6TlKPNyQ)**   
  Specify the semantic indexing configuration settings you want to apply when AI Search indexes records from your indexed sources.
* **[AI Search RAG (Retrieval-Augmented Generation)](https://www.servicenow.com/docs/3YjiJE9YF1LXTJPgXiBYWQ)**   
  The AI Search RAG (Retrieval-Augmented Generation) capability provides a framework that enhances the search accuracy of AI Search results by narrowing the focus of your large language model (LLM) to a specific, contextual data set, instead of the vast, general data it was trained on.

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