---
sourceDocument: Zurich ServiceNow AI Platform Administration
sourceDocumentLink: https://www.servicenow.com/docs/r/zurich/platform-administration
Release :
- zurich
ft:locale :
- en-US
ft:publication_title :
- Zurich ServiceNow AI Platform Administration
ft:clusterId :
- platadm
bundleId :
- platadm
workflow :
- Platform
---
# Semantic vector search
# Semantic vector search in AI Search {#ariaid-title1}
Release version: Zurich
Updated July 6, 2026
 3 minutes to read
Summarize
 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 vector search in AI Search
Semantic vector search in AI Search enhances the Now LLM Service's ability to find results based on the meaning and context of search terms rather than relying on exact keyword matches.
This advanced search mode interprets natural language to improve recall and relevance by identifying semantic similarities between search queries and indexed content.
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## Key Features
* **Meaning-based Search:** Instead of matching literal keywords, semantic vector search analyzes the intent and context of terms to retrieve more relevant results.
* **Improved Recall:** It returns results that are semantically similar even if the exact keywords are not present, enhancing retrieval of knowledge articles, catalog items, and topics.
* **Automatic Similarity Detection:** Unlike keyword search, it does not require synonym dictionaries; it automatically detects term similarities during indexing.
* **Integration with Now LLM Service:** Used in ServiceNow Otto® for Virtual Agent, Genius Results for Knowledge Base Articles, and Actions Genius Results.
* **Overrides Keyword Search:** Semantic vector search results are ranked by computed similarity to the query rather than traditional keyword relevancy scores.
* **Seamless Enablement:** This search mode is automatically enabled with no additional configuration needed.
* **Support for External Content Connectors:** Semantic vector indexing extends to documents retrieved from external systems, benefiting features using the Now LLM Service.
## Key Outcomes
* Provides a more natural, consistent search experience by returning relevant results for varied queries sharing the same underlying meaning.
* Reduces administrative overhead by eliminating the need to maintain synonym dictionaries.
* Enhances the effectiveness of virtual agents and knowledge retrieval by better understanding user intent.
* Enables prioritized and contextually accurate search results, improving user satisfaction and productivity.
Semantic vector search allows 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.
By default, AI Search uses keyword search, meaning that it finds results for records that contain the best matches for the keywords (terms) in your search query. Term matching doesn't take into account the context
or meaning of your search terms.
Starting in the Vancouver Patch 4 release, AI Search includes an alternate search mode called semantic vector search that's used in features which work with the Now LLM Service. Examples of such features include
ServiceNow Otto® for
Virtual Agent chats,
ServiceNow Otto Summary Genius Results, and Actions Genius Results.
Semantic vector search analyzes the meanings and context of your search terms and uses that information to find results with similar meanings. It improves search recall by interpreting natural language to more accurately reflect
your search's intent.
As an example, suppose you index a source record including text how to prevent phishing and search for avoiding scams.
* In keyword search mode, AI Search returns no result for this record because your search query terms aren't literal matches for the terms included in the indexed content.
* In semantic vector search mode, however, AI Search might return a result for the record based on the following context-aware analysis:
* Your search term avoiding is contextually similar in meaning to prevent
* Your search term scams has meaning that overlaps with the meaning of phishing
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AI Search orders results from semantic vector search based on how similar they are to your search. In the example, if you had a second indexed record including text how to prevent scams,
it would be more similar to the avoiding scams search and its search result would appear before the how to prevent phishing result.
Unlike the default keyword-based search mode, semantic vector search doesn't rely on your synonym dictionaries to find term equivalences. AI Search identifies term similarities automatically when indexing source content and metadata for semantic vector search.
Semantic vector search is automatically enabled and doesn't include any configurable settings.
## Benefits of semantic vector search {#semantic-search-ais__section_rcp_ysz_3zb}
In comparison with the default keyword search mode, semantic vector search provides the following benefits:
* Helps improve recall for knowledge article, Catalog Item, and topic retrieval searches by matching results based on the underlying meaning of your search rather than matching on keywords.
* Provides a more natural and consistent search experience by returning similar results for varied search queries with the same underlying meaning.
* Reduces the necessity to create and maintain synonym dictionaries to anticipate similarities in searches.
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## Semantic vector search output {#semantic-search-ais__section_oh5_m1y_fzb}
Semantic vector search overrides the normal AI Search term matching and relevancy ranking mechanisms. Results are ordered based on their computed similarity to your search query terms, with the most similar results appearing first.
## Availability of semantic vector search {#semantic-search-ais__section_jzv_q1y_fzb}
Semantic vector search is available in the following contexts.
* ServiceNow Otto for Virtual Agent uses semantic vector search for Catalog Item retrieval and live agent topic retrieval. For more details, see [ServiceNow Otto for Virtual Agent](https://www.servicenow.com/docs/access?context=now-assist-in-va-landing&version=zurich&pubname=zurich-conversational-interfaces&ft:locale=en-US).
* Knowledge base articles Genius Results use semantic vector search along with legacy keyword search when looking for knowledge articles that match your search query. They also use semantic vector search when looking for cached answers that match your query in the second-level cache. For more details, see [Knowledge base articles Genius Results](https://www.servicenow.com/docs/cXr5CgwHjlxaueQ8TpPyRQ "Knowledge base articles Genius Results use the LLM to generate concise, actionable answers from knowledge article results in Service Portal, Virtual Agent, Employee Center, and global searches.") and [Caching for Now Assist Q\&A Genius Results](https://www.servicenow.com/docs/Altt5Ujy~MTJYzztqdBZUQ#caching-now-assist-q-a-gr "AI Search provides two query-time caches to improve search performance for Now Assist Q&A Genius Results. Caching enables AI Search to return previously generated Now Assist Q&A Genius Result answers without submitting knowledge articles to the Now LLM Service for answer generation.").
* Actions Genius Results use semantic vector search along with legacy keyword search when looking for Catalog Items that match your search query. For more details, see [Actions Genius Results](https://www.servicenow.com/docs/lIxe1UVcGXjfmxbzOWZG5Q "Now Assist Actions Genius Results display actionable answers showing Catalog Items and Virtual Agent topics that match your search query. AI Search matches records based on their similarity to your search's intent and meaning instead of looking for exact term matches.").
* All connectors from the External Content Connectors application support semantic vector indexing when retrieving document content from external source systems. Only features which use semantic vector search with the Now LLM Service can take advantage of this support. For more information on external content connector configuration and usage, see [External Content Connectors](https://www.servicenow.com/docs/WTuffEMuT45YcR6uZYFClg "The External Content Connectors ServiceNow Store application enables AI Search applications to search content and metadata from supported external source systems, such as Atlassian Confluence Cloud and Microsoft SharePoint Online.").
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