Semantic vector search in AI Search
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Summary of Semantic vector search in AI Search
Semantic vector search in AI Search enhances the Now LLM Service by enabling search results based on the meaning and context of search terms rather than simple keyword matches. This advanced search mode improves recall and relevance by interpreting natural language queries to find content with similar semantic meaning.
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Key Features
- Meaning-based search: Unlike traditional keyword search, semantic vector search analyzes the intent and context behind search queries to deliver more relevant results.
- Automatic similarity detection: It automatically identifies term and concept similarities during indexing without relying on manually maintained synonym dictionaries.
- Integrated with Now Assist: Used in Now Assist Q&A Genius Results, Now Assist in Virtual Agent, and Now Assist Actions Genius Results to improve knowledge article, catalog item, and topic retrieval.
- External content support: Works with External Content Connectors for semantic vector indexing of documents from external systems, enhancing the scope of semantic search.
- Automatic enablement: Semantic vector search is enabled by default with no additional configuration needed.
Key Outcomes
- Improved search recall: Retrieves results that match the underlying meaning of queries, even when keywords differ, leading to better discovery of relevant information.
- Consistent user experience: Provides more natural and reliable search results across varied queries with similar intent.
- Reduced maintenance: Eliminates the need to create and maintain extensive synonym dictionaries for effective search results.
- Optimized result ranking: Orders search results by computed semantic similarity, ensuring the most relevant content appears first.
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 chats, 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.
- 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
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 does not 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
- 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.
Semantic vector search output
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
- 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.
- 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 and Caching for Now Assist Q&A Genius Results.
- 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.
- 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.