--- sourceDocument: Australia ServiceNow AI Platform Administration sourceDocumentLink: https://www.servicenow.com/docs/r/australia/platform-administration Release : - australia ft:locale : - en-US ft:publication_title : - Australia 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: Australia Updated September 10, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Semantic index configuration for indexed sources ServiceNow's AI Search offers a generalized Retrieval-Augmented Generation (RAG) framework that enables administrators to configure semantic indexing settings for records sourced from AI Platform® tables. This configuration, called asemantic index configuration, determines how content from indexed sources is processed and indexed to support semantic vector search, which improves search relevance by finding results with meanings similar to the search terms. Show full answer Show less ## Semantic index configuration components * **Indexed source:** A reference to an existing indexed source containing field values or attachments to be indexed for semantic vector search. * **Embedding models:** One or more models that encode the indexed content into vector representations, enabling semantic vector search to understand the meaning of the content. * **Chunking strategy and parameters:** Defines how text from the indexed source is broken into smaller chunks during indexing. Chunking enhances search context and relevancy by managing content size and structure effectively. * **Semantic index fields:** Settings for individual fields or attachments from the indexed source that specify how each should be semantically indexed and the order in which they are evaluated during indexing. Multiple semantic index configurations can be defined for a single indexed source, but additional configurations may increase indexing workload and impact performance. ## Activation and configuration The semantic index configuration feature is enabled through the AI Search Semantic Controller plugin (*com.glide.ais.semanticsearch*), which activates automatically when any ServiceNow Otto® application is installed. Administrators can verify activation by checking for the Semantic Index Configuration related list on indexed source records. Administrators specify semantic indexing settings on indexed sources to control how AI Search processes and indexes content to improve semantic vector search capabilities. ## Practical benefits for ServiceNow customers * Enables enhanced search accuracy by leveraging semantic understanding of indexed content rather than relying solely on keyword matching. * Improves the relevance of AI Search results by using vectorized content representations and chunking strategies that preserve contextual meaning. * Provides flexible configuration options for embedding models and content chunking, allowing tuning based on the nature of indexed data. * Supports integration with the AI Search RAG framework, which further refines search results by augmenting large language model (LLM) context with relevant indexed data. 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/et~v628Hw6Q5AVlidprgVg "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/2DCULyElDqaFQk0zc_7vQw "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=australia&pubname=australia-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/FCz4vqBVFx_rBmrku4r_XQ)** 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/ZnJ4DGHiIq3to2j1ncN5UQ)** The AI Search RAG (Retrieval-Augmented Generation) capability provides a framework that enhances the search accuracy of AI Search results by using relevant indexed data to provide context for the LLM. *[\>]: and then