---
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 vector search
# Semantic vector search in AI Search {#ariaid-title1}
Release version: Brazil
Updated September 10, 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 utilizes the Now LLM Service to find search results based on the meaning and context of your query rather than just keyword matching.
This advanced search method enhances recall and accuracy for knowledge articles, catalog items, and virtual agent interactions by interpreting natural language queries to better reflect user intent.
Show full answer Show less
## Key Features
* **Meaning-based matching:** Unlike traditional keyword search, semantic vector search identifies results with similar meanings to the search terms, improving retrieval of relevant content even when exact keywords do not match.
* **Context-aware ranking:** Results are ordered by how closely their content semantically aligns with the search query.
* **Automatic term similarity detection:** Eliminates the need for manual synonym dictionaries by automatically identifying similarities during indexing.
* **Seamless integration:** Enabled automatically with no configuration required, available in key ServiceNow features such as Otto for Virtual Agent, Genius Results for knowledge base articles and actions, and external content connectors.
## Practical Benefits for ServiceNow Customers
* Improves search recall by capturing the intent behind varied search queries, resulting in more relevant and comprehensive results.
* Provides a consistent and natural search experience across knowledge management, service catalog retrieval, and virtual agent interactions.
* Reduces administrative overhead by removing the need to manually manage synonym dictionaries.
* Enhances virtual agent effectiveness by enabling more accurate topic and catalog item retrieval during live chats.
## Where Semantic Vector Search Is Applied
* **ServiceNow Otto for Virtual Agent:** Used for catalog item and live agent topic retrieval.
* **Genius Results for Knowledge Articles:** Combines semantic vector search with legacy keyword search to find relevant articles and cached answers.
* **Actions Genius Results:** Uses semantic vector search alongside keyword search to retrieve catalog items.
* **External Content Connectors:** Support semantic vector indexing for external document content, leveraged by features using the Now LLM Service.
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.
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 account for the context
or meaning of your search terms.
Starting with 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, 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
the intent of your search.
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
{#semantic-search-ais__ul_tbm_tjb_hzb}
{#semantic-search-ais__ul_r3h_rjb_hzb}
AI Search orders results from semantic vector search based on how similar they are to your search. In the example, suppose you had a second indexed record including text how to prevent scams. This record 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 enabled automatically and has no configurable settings.
## Benefits of semantic vector search {#semantic-search-ais__section_rcp_ysz_3zb}
Compared to 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.
{#semantic-search-ais__ul_hk4_zsz_3zb}
## 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 .
* 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/5yAXceeV_UTGZYxLUskGVw "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 Knowledge base articles Genius Results](https://www.servicenow.com/docs/QKvJu22NwjeAVdv8eQ7aow#caching-now-assist-q-a-gr "AI Search provides two query-time caches to improve search performance for Knowledge base articles Genius Results. Caching enables AI Search to return previously generated 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/57NXMMfzvyLpffbyb~oR1w "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/7P4g5FZQtNJaK16G97BYwQ "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.").
{#semantic-search-ais__ul_n53_ltz_3zb}