--- sourceDocument: Yokohama ServiceNow AI Platform Administration sourceDocumentLink: https://www.servicenow.com/docs/r/yokohama/platform-administration Release : - yokohama ft:locale : - en-US ft:publication_title : - Yokohama 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: Yokohama * * Updated October 3, 2025 * * ![](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 The semantic index configuration allows AI Search administrators to set up how content from indexed sources is processed for semantic vector search. This configuration enhances the search experience by utilizing specific settings tailored to the indexed source's data. Show full answer Show less ## Key Features * **Indexed Source:** Refers to existing sources with relevant field values or attachments intended for indexing. * **Embedding Models:** Specifies one or more models that encode information from the indexed source into vector maps, enabling semantic vector search. * **Chunking Strategy:** Defines how content is divided into smaller portions (chunks) during indexing, improving search load and relevance. * **Semantic Index Fields:** Identifies fields from the indexed source that dictate semantic indexing settings, allowing for evaluation order specification. * **Multiple Configurations:** You can create multiple semantic index configurations for an indexed source, but additional configurations may increase indexing performance costs. ## Activating Semantic Index Configuration The AI Search Semantic Controller plugin (com.glide.ais.semanticsearch) activates semantic index configuration automatically when any Now Assist application is installed. To verify activation, navigate to All \> AI Search \> AI Search Index \> Indexed Sources and check for the Semantic Index Configuration related list on the indexed source form. ## Next Steps To configure semantic indexing settings for your indexed sources, specify the desired configuration parameters. Additionally, consider using the AI Search Retrieval Augmented Generation (RAG) application to improve search accuracy by focusing on specific datasets. 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/hKmPER6CuDD8sNqtd_4w0g "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/b0RkichzQgaXTW2UBEhiRw "Semantic vector search allows the Now LLM Service to find results based on how similar they are in meaning to your search terms. Now Assist Q&A Genius Results and Now Assist in 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 [Now Assist application](https://www.servicenow.com/docs/access?context=platform-now-assist-landing&version=yokohama&pubname=yokohama-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/TcWjjPwH0IKmA4L6_axmTA)** Specify the semantic indexing configuration settings you want to apply when AI Search indexes records from your indexed sources. * **[AI Search Retrieval Augmented Generation (RAG)](https://www.servicenow.com/docs/why7htuWUhqURqmpjmvzXg)** You can enhance the search accuracy of your AI Search results by using the AI Search Retrieval Augmented Generation (RAG) application. With RAG, you can limit a large language model's (LLM's) focus to a specific, contextual dataset, instead of the broad, general data that it was trained on. *[\>]: and then