Cómo filtrar búsquedas por relevancia a nivel del documento
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Cuando realizas búsquedas en tu app de Vertex AI Search, puedes aplicar un umbral de relevancia para que solo se muestren como resultados los documentos que cumplan con este umbral. En esta página, se explica cómo especificar un umbral de relevancia para reducir la cantidad de documentos que se muestran en las búsquedas.
Acerca del filtrado por relevancia a nivel del documento
A cada documento que devuelve una búsqueda se le asigna un nivel de relevancia, que indica la relevancia del documento devuelto para la búsqueda. Cuando realizas una consulta a través de una llamada a la API, puedes establecer un umbral de relevancia. Establecer un umbral de relevancia alto puede reducir la cantidad de documentos que muestra una búsqueda.
Por ejemplo, si observas que la búsqueda devuelve demasiados documentos que no son lo suficientemente relevantes para tus usuarios, establece el umbral de relevancia en alto para reducir los resultados solo a los pocos que sean más pertinentes. Si el parámetro de configuración alto es demasiado restrictivo, prueba con el parámetro medio.
Tipos de datos y apps compatibles con el filtro de relevancia a nivel del documento
El filtro de relevancia a nivel del documento se puede aplicar a los almacenes de datos con los siguientes tipos de datos:
Datos del sitio web con indexación avanzada de sitios web
Datos no estructurados personalizados
Datos estructurados personalizados
El filtro de relevancia a nivel del documento no funciona para los almacenes de datos con indexación básica de sitios web, datos de medios o datos de atención médica.
Además, el filtro de relevancia a nivel del documento no se puede usar con las apps de búsqueda combinada. Las apps de búsqueda combinada son apps que están conectadas a varios almacenes de datos.
Otros tipos de filtros
El filtro de relevancia a nivel del documento no es la única forma en que puedes filtrar los datos que muestran las búsquedas. También puedes usar expresiones de filtro para filtrar los resultados según los metadatos (en la indexación avanzada de sitios web y los almacenes de datos no estructurados con metadatos) y los valores de los campos (en los almacenes de datos estructurados).
Para obtener más información, consulta los siguientes recursos:
Si usas una expresión de filtro y el filtro de relevancia a nivel del documento, primero se aplica la expresión de filtro a los resultados y, luego, se aplica el filtro de relevancia a nivel del documento.
Aquí, el umbral de relevancia se establece en alto, por lo que solo se muestran los resultados más relevantes. En este ejemplo, solo se determinó que un documento era muy pertinente.
Prueba varias búsquedas con diferentes umbrales para determinar la mejor configuración de umbral para tus datos y tu aplicación.
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2025-09-08 (UTC)"],[[["\u003cp\u003eVertex AI Search allows filtering search results by document-level relevance, reducing the number of returned documents based on their relevance to the query.\u003c/p\u003e\n"],["\u003cp\u003eYou can set the relevance threshold to \u003ccode\u003eHIGH\u003c/code\u003e, \u003ccode\u003eMEDIUM\u003c/code\u003e, \u003ccode\u003eLOW\u003c/code\u003e, or \u003ccode\u003eLOWEST\u003c/code\u003e when making an API call to narrow down the search results to only the most relevant ones, using the \u003ccode\u003erelevanceThreshold\u003c/code\u003e field.\u003c/p\u003e\n"],["\u003cp\u003eThis document-level relevance filter is applicable to data stores with website data with advanced indexing, generic unstructured data, and generic structured data, but it is not supported for blended search apps, or data stores with basic website indexing, media data, or healthcare data.\u003c/p\u003e\n"],["\u003cp\u003eDocument-level relevance filtering can be used alongside filter expressions based on metadata or field values, with filter expressions being applied first.\u003c/p\u003e\n"],["\u003cp\u003eTo use this filtering method, search over an app using the \u003ccode\u003eengines.servingConfigs.search\u003c/code\u003e method, and input your app ID and query alongside the relevance threshold.\u003c/p\u003e\n"]]],[],null,["# Filter searches by document-level relevance\n\n| **Note:** This feature is a Preview offering, subject to the \"Pre-GA Offerings Terms\" of the [GCP Service Specific Terms](https://cloud.google.com/terms/service-terms). Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages). Further, by using this feature, you agree to the [Generative AI Preview terms and conditions](https://cloud.google.com/trustedtester/aitos) (\"Preview Terms\"). For this feature, you can process personal data as outlined in the [Cloud Data Processing Addendum](https://cloud.google.com/terms/data-processing-terms), subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).\n|\n| \u003cbr /\u003e\n|\nWhen searching in your Vertex AI Search app, you can apply a\nrelevance threshold so that only the documents that meet this threshold\nare returned as results. This page explains how to specify a\nrelevance threshold in order to reduce the number of documents returned in\nqueries.\n\nAbout filtering by document-level relevance\n-------------------------------------------\n\nEach document returned by a search query is given a relevance level, which\nindicates the relevance of the returned document to the query. When you make a\nquery through an API call, you can set a relevance threshold. Setting a high\nrelevance threshold can reduce the number of documents returned by a query.\n\nFor example, if you find that search is returning too many documents of\ninsufficient relevance to your users, set the relevance threshold to high to\nnarrow the results to only those few that are most relevant. If the high setting\nis too restrictive, try the medium setting.\n| **Note:** This document-level relevance filtering feature is different from and less precise than the [document-relevance score](/generative-ai-app-builder/docs/preview-search-results#relevance-scores) that can be returned for search results.\n\nData types and apps supported for document-level relevance filter\n-----------------------------------------------------------------\n\nThe document-level relevance filter can be applied to data stores with following kinds of data:\n\n- Website data with advanced website indexing\n- Custom unstructured data\n- Custom structured data\n\nThe document-level relevance filter doesn't work for data stores with basic website indexing,\nmedia data, or healthcare data.\n\nFurthermore, the document-level relevance filter can't be used with blended search apps. Blended\nsearch apps are apps that are connected to multiple data stores.\n\nOther kinds of filters\n----------------------\n\nThe document-level relevance filter is not the only way you can filter data returned by queries. You\ncan also use filter expressions to filter results based on metadata (in\nadvanced website indexing and unstructured data with metadata data stores) and field\nvalues (in structured data stores).\n\nFor information, see:\n\n- [Filter expressions with advanced website indexing](/generative-ai-app-builder/docs/filter-website-search#filter-expressions-advanced-indexing)\n\n- [Filter custom search for structured or unstructured data](/generative-ai-app-builder/docs/filter-search-metadata)\n\nIf you use both a filter expression and the document-level relevance filter, the filter expression\nis applied first to the results and then the document-level relevance filter is applied.\n\nBefore you begin\n----------------\n\nMake sure you have created an app and data store and have ingested data\ninto your data store. For more information, see [Create a search\napp](/generative-ai-app-builder/docs/create-engine-es). See also [Data types and apps supported for\ndocument-level relevance filter](#supported).\n\nSearch and filter results by document-level relevance\n-----------------------------------------------------\n\nTo filter by relevance, follow these steps:\n| **Note:** You can search over an app using the [`engines.servingConfigs.search`](/generative-ai-app-builder/docs/reference/rest/v1/projects.locations.collections.engines.servingConfigs/search) method and you can search over a data store using the [`dataStores.servingConfigs.search`](/generative-ai-app-builder/docs/reference/rest/v1/projects.locations.collections.dataStores.servingConfigs/search) method. For the following procedure, Google recommends searching using the `engines.servingConfigs.search` method.\n\n1. Find your app ID. If you already have your app ID, skip to the next step.\n\n 1. In the Google Cloud console, go to the **AI Applications** page.\n\n [Go to Apps](https://console.cloud.google.com/gen-app-builder/engines)\n 2. On the **Apps** page, find the name of your app and get the app's ID from\n the **ID** column.\n\n2. To filter search by document-level relevance, use the `relevanceThreshold`\n field with the [`engines.servingConfigs.search`](/generative-ai-app-builder/docs/reference/rest/v1alpha/projects.locations.collections.engines.servingConfigs/search) method.\n\n **Key Term:** In Vertex AI Search, the term *app* can be used interchangeably with the term *engine* in the context of APIs. \n\n curl -X POST -H \"Authorization: Bearer $(gcloud auth application-default print-access-token)\" \\\n -H \"Content-Type: application/json\" \\\n \"https://discoveryengine.googleapis.com/v1alpha/projects/\u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e/locations/global/collections/default_collection/engines/\u003cvar translate=\"no\"\u003eAPP_ID\u003c/var\u003e/servingConfigs/default_search:search\" \\\n -d '{\n \"servingConfig\": \"projects/\u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e/locations/global/collections/default_collection/engines/\u003cvar translate=\"no\"\u003eAPP_ID\u003c/var\u003e/servingConfigs/default_search\",\n \"query\": \"\u003cvar translate=\"no\"\u003eQUERY\u003c/var\u003e\",\n \"relevanceThreshold\": \"\u003cvar translate=\"no\"\u003eRELEVANCE_THRESHOLD\u003c/var\u003e\"\n }'\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: the ID of your Google Cloud project.\n - \u003cvar translate=\"no\"\u003eAPP_ID\u003c/var\u003e: the ID of the Vertex AI Search app that you want to query.\n - \u003cvar translate=\"no\"\u003eQUERY\u003c/var\u003e: the query text to search.\n - \u003cvar translate=\"no\"\u003eRELEVANCE_THRESHOLD\u003c/var\u003e: one of the following: `HIGH`, `MEDIUM`, `LOW`, `LOWEST`.\n\n #### Example command and result\n\n ```\n curl -X POST -H \"Authorization: Bearer $(gcloud auth print-access-token)\"\n -H \"Content-Type: application/json\" \\\n \"https://discoveryengine.googleapis.com/v1alpha/projects/my-project-123/locations/global/collections/default_collection/engines/my-search-app/servingConfigs/default_search:search\" \\\n -d '{\n \"servingConfig\": \"projects/my-project-123/locations/global/collections/default_collection/engines/my-search-app/servingConfigs/default_search\",\n \"query\": \"What is the check grounding API\",\n \"relevanceThreshold\": \"HIGH\"\n }'\n\n {\n \"results\": [\n {\n \"id\": \"a082e70352c073a4443502477255bd2a\",\n \"document\": {\n \"name\": \"projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/a082e70352c073a4443502477255bd2a\",\n \"id\": \"a082e70352c073a4443502477255bd2a\",\n \"derivedStructData\": {\n \"displayLink\": \"cloud.google.com\",\n \"link\": \"https://cloud.google.com/generative-ai-app-builder/docs/check-grounding\",\n \"htmlTitle\": \"Check grounding\",\n \"title\": \"Check grounding\"\n }\n }\n }\n ],\n \"totalSize\": 1,\n \"attributionToken\": \"f_B-CgwIidzwswYQyue15gESJDY2N2M1NmJkLTAwMDAtMjk3Ni1iMGI4LTg4M2QyNGZmNTZhOCIHR0VORVJJQypAjr6dFavEii3b7Ygt3o-aIoCymiLC8J4Vo4CXIra3jC3Usp0V24-aIt7tiC3n7YgtrsSKLeTtiC2DspoixsvzFw\",\n \"guidedSearchResult\": {},\n \"summary\": {}\n }\n ```\n\n Here, the relevance threshold is set to high, so only the most\n relevant results are returned. In this example, only one document was determined\n to be highly relevant.\n3. Test multiple queries with different thresholds to determine the best\n threshold settings for your data and application."]]
