![]() They have since fallen out of fashion due to many problems, including accessibility, security, file size, and more. Embedding these technologies was achieved through elements like, and the lesser-used, and they were very useful at the time. They did however have many problems, which far outweighed any positives as network speeds got faster, so you don't see them being used anymore.Ī little while later (late 90s, early 2000s), plugin technologies became very popular, such as Java Applets and Flash - these allowed web developers to embed rich content into webpages such as videos and animations, which just weren't available through HTML alone. ![]() These were considered the height of coolness in the mid to late 90s, and there was evidence that having a webpage split up into smaller chunks like this was better for download speeds - especially noticeable with network connections being so slow back then. These were embedded in a master document called a frameset, which allowed you to specify the area on the screen that each frame filled, rather like sizing the columns and rows of a table. Table 3.A long time ago on the Web, it was popular to use frames to create websites - small parts of a website stored in individual HTML pages. New global classes and additional methods to existing global classes Class New scoped classes and additional methods to existing scoped classes Class Tab filter AI Search components in custom applications created with UI Builder. Add AI SearchĬomponents to your custom-built application Designers can include search input, search results, search facets, and navigation The most relevant search results within a record producer. In Service Portal using the AI Search Assist widgetĪssist Improve incident deflection for Service Portal users by displaying Search results using hit-highlighting Tag content that matched a user's search query terms. Specific searches with result improvement rules Search administrators can configure conditional query rules to boost, promote, orīlock search results for specific searches or user contexts. Interactions and tunes relevancy scores to better suit user usage patterns. Results with machine learning relevancy Search results appear in relevancy-sorted order. Refine search results usingĭynamic facet filters Configure facets for search applications to let users refine their searches usingįacet filters dynamically generated from the current set of search results. Navigation tab filters Configure navigation tab filters for search applications to let users refine their Relevant information and actions for search users. Search query as actionable Genius Results cards Optionally enable Genius Results to determine search intent for people, ServiceNow® Knowledge, and Catalog Item searches and display answer cards with Search query terms Replace misspelled user search query terms with terms found in searchable Automatically remove these terms from users' search queries to improve Queries Configure dictionaries of overly frequent terms that do not produce meaningful ![]() It easier for users to find the answers they need. Synonyms for query terms Natural-language processing matches variant word forms and equivalent terms, making Improve search recall with lemmaĪnd Unicode normalization Normalization automatically expands user search query terms to match root word formsĪnd alternate Unicode compositions. ![]() Users or from the user's personal search history. Queries with auto-complete Auto-complete provides users with suggested queries and results aggregated from all Integrate search into Now Platform applications Set AI Search as the search engine for Service Portal, Install the ECS applicationįrom the ServiceNow Store to additionally index content from external Microsoft SharePoint repositories. Repositories Configure tables as indexed sources and retrieve searchable content from their Now Platform tables and Microsoft SharePoint
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