--- sourceDocument: Australia ServiceNow AI Platform Administration sourceDocumentLink: https://www.servicenow.com/docs/r/australia/platform-administration Release : - australia ft:locale : - en-US ft:publication_title : - Australia ServiceNow AI Platform Administration ft:clusterId : - platadm bundleId : - platadm workflow : - Platform --- # AI Search RAG (Retrieval-Augmented Generation) # AI Search RAG (Retrieval-Augmented Generation) {#ariaid-title1} Release version: Australia Updated September 10, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) 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 AI Search RAG (Retrieval-Augmented Generation) AI Search RAG enhances the accuracy of AI Search results by integrating relevant indexed data as context for large language models (LLMs). It improves search capabilities by combining advanced information retrieval with text generation, enabling more precise and context-rich responses for user queries. Show full answer Show less ## Key Features * **Indexing:** Organizes data into searchable formats using the `aisdatasource` table and semantic indexing configurations, preparing sources for efficient retrieval. * **Search Process:** Utilizes a RAG retrieval API to find and rank the most relevant results from indexed data based on user queries. * **Core Components:** * **Query Rewrite:** Simplifies complex or lengthy queries to prevent token limit issues with embedding models. * **Retriever:** Fetches relevant indexed results using the refined query. * **Re-ranker:** Applies LLM capabilities to further refine and rank the retrieved results before delivering the final response. * **Embedding Model:** Employs semantic and vector search techniques to generate embeddings that represent relevant data as vectors. ServiceNow's default embedding model is the ServiceNow Embedding (E5), with support for third-party models like Azure OpenAI and Gemini Text Embedding. Custom third-party embedding models can also be integrated. ## Practical Application for ServiceNow Customers By activating the AI Search RAG plugin (`snaisrag`), which is automatically enabled with the Generative AI Controller or any ServiceNow Otto® application, customers gain advanced AI-enhanced search capabilities. This enables your ServiceNow instance to deliver more accurate, context-aware answers by aligning indexed data retrieval with generative AI, improving user experience in knowledge discovery and information retrieval workflows. The AI Search RAG (Retrieval-Augmented Generation) capability provides a framework that enhances the search accuracy of AI Search results by using relevant indexed data to provide context for the LLM. ## AI Search RAG overview {#ais-rag__section_mjd_tfh_ggc} RAG (Retrieval Augmented Generation) technology helps enhance search capabilities of your AI Search application by combining information retrieval with text generation. It includes the following steps: Indexing : Prepare and organize data to make it searchable. It includes the ais_datasource table and semantic indexing configurations. For more information, see [Indexed sources in AI Search](https://www.servicenow.com/docs/et~v628Hw6Q5AVlidprgVg "Indexed sources designate ServiceNow AI Platform tables and external document sets with alphanumeric text and string field content that you want to make searchable. AI Search ingests text and string fields from table records or external documents and stores their searchable alphanumeric content in its search index."). Searching : Finds the most relevant results from indexed data for a query. It includes a RAG retrieval API that searches indexed data and RAG capabilities that retrieve and rank results. Components of AI Search RAG The AI Search RAG capability orchestrates the complete RAG process using components, such as query rewrite, retriever, and re-ranker to deliver the most relevant results. * Query Rewrite: Rewrites the complex or long queries into concise, short form to avoid truncation issues with the embedding model's token limit. * Retriever: Uses the rewritten query to retrieve the most relevant results from indexed sources. * Re-ranker: Uses LLM to further refine the ranking of retrieved results. * Final Response: The output of the re-ranker is returned as the final response. {#ais-rag__ul_zc4_3b3_ggc} The following diagram shows how AI Search RAG components work together. The AI Search RAG system works by first organizing data into a searchable format using specific indexing configurations. When a user asks a question, the system processes it and may rephrase the query for better understanding. It then searches for relevant information with the help of retriever and the re-ranker uses AI to arrange them in the most useful order. All of these steps work together under the guidance of a search profile to produce an accurate and context-rich answer for the user. ## Embedding model in AI Search RAG {#ais-rag__section_ndn_2zk_zfc} AI Search RAG uses the advanced search methods, such as semantic or vector search to find relevant and more context-oriented information from the configured indexed sources. The semantic search further uses embedding model to retrieve the most relevant data based on the user search query for embedding generation. This retrieved information along with the search query then prompts the LLM model to generate the relevant response. In a RAG system, the embedding model helps identify and retrieve relevant information from indexed sources. The retrieved content is represented as vectors and then passed to the LLM to help generate accurate responses. ServiceNow AI Search RAG uses the default ServiceNow Embedding (E5) model. It also supports additional third-party embedding models, such as Azure OpenAI Embedding and Gemini Text Embedding. You can also bring your own third-party hosted custom embedding model to create embeddings for your RAG use case. ## Activating AI Search RAG (Retrieval-Augmented Generation) {#ais-rag__section_fl2_rhl_zfc} AI Search RAG (Retrieval-Augmented Generation) functionality is provided by the AI Search RAG plugin (sn_ais_rag). This plugin is automatically activated for your instance when you install [Generative AI Controller](https://www.servicenow.com/docs/access?context=installing-generative-ai-controller&version=australia&pubname=australia-intelligent-experiences&ft:locale=en-US) or any [ServiceNow Otto® application](https://www.servicenow.com/docs/access?context=platform-now-assist-landing&version=australia&pubname=australia-intelligent-experiences&ft:locale=en-US).