--- 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 --- # AI Search RAG (Retrieval-Augmented Generation) # AI Search RAG (Retrieval-Augmented Generation) {#ariaid-title1} Release version: Brazil 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) The AI Search RAG capability enhances the accuracy and relevance of AI Search by focusing large language model (LLM) queries on specific, contextual datasets instead of broad, general training data. It combines information retrieval with text generation to deliver precise, context-rich answers tailored to user queries within ServiceNow. Show full answer Show less ## Key Features * **Indexing:** Organizes and prepares data into searchable formats using the `aisdatasource` table and semantic indexing configurations, enabling efficient retrieval. * **Searching:** Utilizes the RAG retrieval API and RAG capability to query indexed data, applying a multi-step process to enhance result relevance. * **Query Rewrite:** Simplifies complex or lengthy queries into concise forms to avoid token limits in embedding models, improving search performance. * **Retriever:** Fetches the most relevant results from indexed sources based on the rewritten query. * **Re-ranker:** Applies LLM-based refinement to reorder search results for optimal relevance before returning the final response. * **Embedding Models:** Uses semantic or vector search with embedding models to map data into vectors for precise retrieval. ServiceNow supports the default ServiceNow Embedding (E5) model, popular third-party embeddings like Azure OpenAI and Gemini Text Embedding, and allows custom third-party embedding models. ## Activation AI Search RAG functionality is enabled through the `snaisrag` plugin, which is automatically activated when installing the Generative AI Controller or any ServiceNow Otto® application, simplifying deployment and integration into your instance. ## Practical Benefits for ServiceNow Customers * Improves AI Search precision by narrowing the focus of LLMs to relevant, indexed data specific to your ServiceNow environment. * Enables faster and more accurate responses to user queries through advanced query rewriting, retrieval, and ranking mechanisms. * Supports flexible embedding model options, allowing you to leverage ServiceNow's default or custom third-party embeddings for your specific use cases. * Seamlessly integrates with existing ServiceNow applications, requiring minimal setup beyond plugin activation. The AI Search RAG (Retrieval-Augmented Generation) capability provides a framework that enhances the search accuracy of AI Search results by narrowing the focus of your large language model (LLM) to a specific, contextual data set, instead of the vast, general data it was trained on. ## 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/wbgc0tkdQcXHU63sXMrFeA "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 : Find the most relevant results from the indexed data using a query. It includes RAG retrieval API to run search on indexed data of the search profile, and RAG capability that manages the RAG process, including retriever and re-ranker to deliver relevant 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. An embedding model is the engine behind RAG that helps it to search and retrieve the correct information from the indexed sources and embed that information into vector map before sending it to the LLM model. ServiceNow AI Search RAG uses the default ServiceNow Embedding (E5) model and 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=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US) or any [ServiceNow Otto® application](https://www.servicenow.com/docs/access?context=platform-now-assist-landing&version=brazil&pubname=brazil-intelligent-experiences&ft:locale=en-US).