---
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 vector search
# Semantic vector search in AI Search {#ariaid-title1}
Release version: Yokohama
Updated October 24, 2025
 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 just keyword matches.
This search mode is designed to improve recall and accuracy by interpreting natural language queries in a way that reflects the user's intent.
It is integrated into features like Now Assist Q\&A Genius Results, Now Assist in Virtual Agent, and Now Assist Actions Genius Results, starting from the Vancouver Patch 4 release onward.
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## Key Features
* **Meaning-based search:** Uses semantic analysis to find results that are contextually similar to the query, not just literal keyword matches.
* **Improved recall:** Identifies relevant records even when search terms differ but share the same underlying meaning (e.g., "avoiding scams" matches "prevent phishing").
* **Automatic similarity detection:** Removes the dependency on synonym dictionaries by automatically identifying term similarities during indexing.
* **Integrated with Now LLM Service features:** Semantic vector search powers retrieval in Now Assist Virtual Agent, Q\&A Genius Results, and Actions Genius Results.
* **Support for external content:** External Content Connectors allow semantic vector indexing for documents from external sources, enabling richer search results.
* **Automatic enablement:** Semantic vector search is enabled by default with no additional configuration needed.
## Key Outcomes
* **More natural and consistent search experience:** Returns similar results for different queries that share the same meaning, improving user satisfaction.
* **Reduced maintenance effort:** Eliminates the need to build and maintain synonym dictionaries to handle term variations.
* **Enhanced relevancy ranking:** Search results are ordered by computed semantic similarity, ensuring the most contextually relevant results appear first.
* **Broader applicability:** Semantic vector search enhances knowledge article retrieval, catalog item searching, and topic matching within Virtual Agent and other AI-powered Now Assist features.
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.
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 Now Assist in Virtual Agent conversations, Now Assist Multi-Content Response Genius Results, and Now Assist 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 need 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.
* Now Assist in Virtual Agent uses semantic vector search for Catalog Item retrieval and live agent topic retrieval. For more details, see [Now Assist in Virtual Agent](https://www.servicenow.com/docs/access?context=now-assist-in-va-landing&version=yokohama&pubname=yokohama-conversational-interfaces&ft:locale=en-US).
* Now Assist Q\&A 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 [Now Assist Q\&A Genius Results](https://www.servicenow.com/docs/M3yYug03OKsz4GPk5XVC8Q "Now Assist Q&A Genius Results use the Now LLM Service 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/VmZ7zf8Rdqosd5C3RRyt6A#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.").
* Now Assist 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 [Now Assist Actions Genius Results](https://www.servicenow.com/docs/2pYMbh05vUTDb3TO7cHAPw "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/QFXZapPl9GI8hahyMx966Q "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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