[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-05(UTC)"],[],[],null,["# About retrieval and ranking\n\nThis page describes how retrieval and ranking work together to deliver relevant\nsearch results in Vertex AI Search apps.\n\nOverview\n--------\n\nIn short, retrieval is finding relevant documents, while ranking is\nordering those retrieved documents. Ranking all the available documents\ncan be computationally expensive. Therefore, retrieval and ranking work\nsequentially.\n\nFirst, the search model understands the query and rewrites it.\nThen, depending on the data sources available and the number of indexed\ndocuments in your data store, the model\nretrieves documents in the order of thousands. A relevance score is assigned to\nthe retrieved documents.\n\nThe ranking model then orders the retrieved documents\nand serves the top 400 ranked results. The following image shows how\nthese two processes fit into the search workflow.\n**Figure 1.** Retrieval and ranking in search workflow\n\n### Retrieval methods\n\nRetrieval is the process of selecting a subset of documents from your data store\nthat are relevant to a user's query. Vertex AI Search model\nmanages retrieval for your search apps based on different signals, such as the\nfollowing, and assigns relevance scores:\n\n- **Topicality**: Includes keyword matching, knowledge graphs, and web signals.\n\n- **Embeddings**: Includes embeddings to find conceptually similar content.\n\n- **Cross-attention**: Allows a model to consider the relationship between a\n query and a document to assign a relevance score to the document.\n\n- **Freshness**: Involves ascertaining the age of the documents in the data\n store.\n\n- **User events**: Includes conversion signals used for personalization.\n\nAdditionally, in a search request, you can supply\n[relevance filters](/generative-ai-app-builder/docs/filter-by-relevance) and\nmetadata filters for [website data](/generative-ai-app-builder/docs/filter-website-search)\nand [structured or unstructured data](/generative-ai-app-builder/docs/filter-search-metadata) to\nnarrow down the list of relevant documents.\n\n### Ranking methods\n\nRanking takes the documents that are selected during the retrieval phase,\nassigns them a new relevance score according to the following conditions, and\nreorders them:\n\n- **Boost** : Promotes and demotes certain results according to custom attributes or freshness. This impacts the first 1,000 retrieved documents and ranks the top 400. For more information, see [Boost search results](/generative-ai-app-builder/docs/boost-search-results).\n- **Custom ranking** : Controls, tunes, and overrides the default ranking logic with a formula-based ranking algorithm to suit your specific requirements. The relevance score that custom ranking assigns takes a precedence when serving the results. For more information, see [Customize search results ranking](/generative-ai-app-builder/docs/custom-ranking).\n- **Search tuning** : Impacts how the model perceives the semantic relevance of your documents and changes the embedding relevance scores. For more information, see [Improve search results with search tuning](/generative-ai-app-builder/docs/search-tuning).\n- **Event-based reranking**: Updates the results at the time of serving using user-events-based personalization model."]]
