Filter searches by document-level relevance

When searching in your Vertex AI Search app, you can apply a relevance threshold so that only the documents that meet this threshold are returned as results. This page explains how to specify a relevance threshold in order to reduce the number of documents returned in queries.

About filtering by document-level relevance

Each document returned by a search query is given a relevance level, which indicates the relevance of the returned document to the query. When you make a query through an API call, you can set a relevance threshold. Setting a high relevance threshold can reduce the number of documents returned by a query.

For example, if you find that search is returning too many documents of insufficient relevance to your users, set the relevance threshold to high to narrow the results to only those few that are most relevant. If the high setting is too restrictive, try the medium setting.

Data types and apps supported for document-level relevance filter

The document-level relevance filter can be applied to data stores with following kinds of data:

• Website data with advanced website indexing
• Custom unstructured data
• Custom structured data

The document-level relevance filter doesn't work for data stores with basic website indexing, media data, or healthcare data.

Furthermore, the document-level relevance filter can't be used with blended search apps. Blended search apps are apps that are connected to multiple data stores.

Other kinds of filters

The document-level relevance filter is not the only way you can filter data returned by queries. You can also use filter expressions to filter results based on metadata (in advanced website indexing and unstructured data with metadata data stores) and field values (in structured data stores).

For information, see:

• Filter expressions with advanced website indexing

• Filter custom search for structured or unstructured data

If you use both a filter expression and the document-level relevance filter, the filter expression is applied first to the results and then the document-level relevance filter is applied.

Before you begin

Make sure you have created an app and data store and have ingested data into your data store. For more information, see Create a search app. See also Data types and apps supported for document-level relevance filter.

Search and filter results by document-level relevance

To filter by relevance, follow these steps:

1. Find your app ID. If you already have your app ID, skip to the next step.

   1. In the Google Cloud console, go to the AI Applications page.

      Go to Apps

   2. On the Apps page, find the name of your app and get the app's ID from the ID column.

2. To filter search by document-level relevance, use the relevanceThreshold field with the engines.servingConfigs.search method.

   curl -X POST -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
   -H "Content-Type: application/json" \
   "https://discoveryengine.googleapis.com/v1alpha/projects/PROJECT_ID/locations/global/collections/default_collection/engines/APP_ID/servingConfigs/default_search:search" \
   -d '{
    "servingConfig": "projects/PROJECT_ID/locations/global/collections/default_collection/engines/APP_ID/servingConfigs/default_search",
   "query": "QUERY",
   "relevanceThreshold": "RELEVANCE_THRESHOLD"
   }'
   

   Replace the following:

   • PROJECT_ID: the ID of your Google Cloud project.
   • APP_ID: the ID of the Vertex AI Search app that you want to query.
   • QUERY: the query text to search.
   • RELEVANCE_THRESHOLD: one of the following: HIGH, MEDIUM, LOW, LOWEST.

   Example command and result

   curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)"
   -H "Content-Type: application/json" \
   "https://discoveryengine.googleapis.com/v1alpha/projects/my-project-123/locations/global/collections/default_collection/engines/my-search-app/servingConfigs/default_search:search" \
   -d '{
   "servingConfig": "projects/my-project-123/locations/global/collections/default_collection/engines/my-search-app/servingConfigs/default_search",
   "query": "What is the check grounding API",
   "relevanceThreshold": "HIGH"
   }'
   
   {
   "results": [
   {
     "id": "a082e70352c073a4443502477255bd2a",
     "document": {
       "name": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/a082e70352c073a4443502477255bd2a",
       "id": "a082e70352c073a4443502477255bd2a",
       "derivedStructData": {
         "displayLink": "cloud.google.com",
         "link": "https://cloud.google.com/generative-ai-app-builder/docs/check-grounding",
         "htmlTitle": "Check grounding",
         "title": "Check grounding"
       }
     }
   }
   ],
   "totalSize": 1,
   "attributionToken": "f_B-CgwIidzwswYQyue15gESJDY2N2M1NmJkLTAwMDAtMjk3Ni1iMGI4LTg4M2QyNGZmNTZhOCIHR0VORVJJQypAjr6dFavEii3b7Ygt3o-aIoCymiLC8J4Vo4CXIra3jC3Usp0V24-aIt7tiC3n7YgtrsSKLeTtiC2DspoixsvzFw",
   "guidedSearchResult": {},
   "summary": {}
   }
   

   Here, the relevance threshold is set to high, so only the most relevant results are returned. In this example, only one document was determined to be highly relevant.

3. Test multiple queries with different thresholds to determine the best threshold settings for your data and application.