This page shows how to use the API to get search summaries with your searchresults. It also explains the options that are available with search summaries.For unstructured and website data only.

For information about getting generative AI answers for your healthcare dataqueries, seeSearch using natural language query with generative AI answer.

Before you begin

Depending on the type of app you have, complete the following requirements:

• For an unstructured search app: Turn on thegenerative responses option.

• For a website search app: Turn on the following features:

  • Enterprise edition features.

  • Generative responses.

  • Advanced website indexing. Requiresdomain verification.

Get a search summary

A search summary is a short summarization of the top one or more search resultsreturned in a search response. The summary itself is taken from the extractiveanswers returned in the response. Therefore, to get a summary, you must also getextractive answers with your search results. For more information, seeGetextractive answers (Preview).

The following image shows the summary when PDFs in a data store are queried withthesummaryResultCount set to5. The summary content can vary depending onthe app configurations.

Search summaries can include Markdown-formatted text and simple HTML tagscommonly understood by Markdown parsers. For this reason, consider using aMarkdown parser in your application to render Markdown text.

To get a search summary, follow these steps:

1. Submit a search request that includescontentSearchSpec.summarySpecand specifies values forsummaryResultCount andmaxExtractiveAnswerCount.For more information about submitting a search request, seeGet search results.

   In the following example,summarySpec indicates that you want a searchsummary and that the summary should be generated from the top three searchresults.

   "contentSearchSpec":{"summarySpec":{"summaryResultCount":3},"extractiveContentSpec":{"maxExtractiveAnswerCount":1}}

   • summaryResultCount: The number of top results to generate the searchsummary from. If the number of results returned is less thansummaryResultCount, the summary is generated from all of the results.

   • maxExtractiveAnswerCount: The number of extractive answers to return foreach search result. The default value is 0 and the maximum is 1.

2. Get the summary from the search response. Onesummary property is returnedin each response.

   Here is an example of a summary returned at the end of a search response:

   "summary":{"summaryText":"BigQuery is Google Cloud's fully managed and completely  serverless enterprise data warehouse. BigQuery supports all data types,  works across clouds, and has built-in machine learning and business  intelligence, all within a unified platform."}

Generate summaries from semantic chunks

You can turn onuse_semantic_chunks to generate summaries from the mostrelevant document chunks. Using semantic chunks for summary generation increasesrecall and retrieval compared to the default behavior of using extractive answers.

When semantic chunking is turned on for summaries, the response returns thesummary and the content of each chunk that the summary used.

To use semantic chunks for summary generation, follow these steps:

1. Submit a search request that includescontentSearchSpec.summarySpec and specifies"use_semantic_chunks": true. For more information about submitting a searchrequest, seeGet search results.

   The following example ofsummarySpec indicates that you want a searchsummary that uses semantic chunks, how many results to include, and whetherto include citations.

   "contentSearchSpec":{"summarySpec":{"useSemanticChunks":SEMANTIC_CHUNK_BOOLEAN,"summaryResultCount":SUMMARY_RESULT_COUNT,"includeCitations":CITATIONS_BOOLEAN,}}

   • SEMANTIC_CHUNK_BOOLEAN: A boolean that specifieswhether to use semantic chunks to generate the search summary. If set totrue, semantic chunks are used.
   • SUMMARY_RESULT_COUNT: The number of top results togenerate the search summary from. The maximum value is10.
   • CITATIONS_BOOLEAN: A boolean that specifies whethercitations are returned. If you turned on chunk mode when you created yourdata store, then citations refer to chunks. Otherwise, citations refer tosource documents. For more about chunk mode, seeParse and chunkdocuments.

2. Get the summary from the search response.

   Here is an example of a search response that includes a summary that isgenerated from chunks and includes citations. Thereferences part of theresponse contains the content of the chunks that the summary is generatedfrom.

   Response

   {"results":[{"id":"123xyz","document":{"name":"projects/exampleproject/locations/global/collections/default_collection/dataStores/exampledatastore/branches/0/documents/123xyz","id":"123xyz","derivedStructData":{"link":"gs://examplebucket/alphabet-investor-pdfs/2004_google_annual_report.pdf"}}}],"totalSize":8375,"attributionToken":"abcdefg","nextPageToken":"hijklmnop","guidedSearchResult":{},"summary":{"summaryText":"Google's search technology uses a combination of techniques to determine the importance of a web page independent of a particular search query and to determine the relevance of that page to a particular search query. [1]","summaryWithMetadata":{"summary":"Google's search technology uses a combination of techniques to determine the importance of a web page independent of a particular search query and to determine the relevance of that page to a particular search query.","citationMetadata":{"citations":[{"endIndex":"216","sources":[{}]}]},"references":[{"document":"projects/exampleproject/locations/global/collections/default_collection/dataStores/exampledatastore/branches/0/documents/123xyz","chunkContents":[{"content":"Groups contains more than 1 billion messages from Usenet Internet discussion groups dating back to 1981.The\ndiscussions in these groups cover a broad range of discourse and provide a comprehensive look at evolving\nviewpoints, debate and advice on many subjects.The new Google Groups adds in the ability to create your own\ngroups for you and your friends and an improved user interface.Google Mobile.Google Mobile offers people the ability to search and view both the "mobileweb,"\nconsisting of pages created specifically for wireless devices, and the entire Google index of more than 8 billion\nweb pages.Google Mobile works on devices that support WAP, WAP 2.0, i-mode or j-sky mobile Internet\nprotocols.In addition, users can access a variety of information using Google SMS by typing a query to the\nGoogle shortcode.Google Mobile is available through many wireless and mobile phone services worldwide.","pageIdentifier":"17"},{"content":"Google Labs is our playground for our engineers and for adventurous Google users.On Google\nLabs, we post product prototypes and solicit feedback on how the technology could be used or improved.Current Google Labs examples include:Google Personalized Search—provides customized search results based on an individual user's interests.Froogle Wireless—gives people the ability to search for product information from their mobile phones\nand other wireless devices.Google Maps—enables users to see maps, get directions, and find local businesses and services quickly\nand easily.Google Maps has several unique features, including draggable maps, integrated local search\nfrom Google Local, and keyboard shortcuts.Google Scholar—enables users to search specifically for scholarly literature, including peer-reviewed\npapers, theses, books, preprints, abstracts and technical reports from all broad areas of research.Google\nScholar can be used to find articles from a wide variety of academic publishers, professional societies,\npreprint repositories and universities, as well as scholarly articles available across the web.Google Suggest—guesses what you're typing and offers suggestions in real time.This is similar to\nGoogle's "Didyoumean?"feature that offers alternative spellings for your query after you search, except\nthat it works in real time.","pageIdentifier":"17"},{"content":"Groups contains more than 1 billion messages from Usenet Internet discussion groups dating back to 1981.The\ndiscussions in these groups cover a broad range of discourse and provide a comprehensive look at evolving\nviewpoints, debate and advice on many subjects.The new Google Groups adds in the ability to create your own\ngroups for you and your friends and an improved user interface.Google Mobile.Google Mobile offers people the ability to search and view both the "mobileweb,"\nconsisting of pages created specifically for wireless devices, and the entire Google index of more than 8 billion\nweb pages.Google Mobile works on devices that support WAP, WAP 2.0, i-mode or j-sky mobile Internet\nprotocols.In addition, users can access a variety of information using Google SMS by typing a query to the\nGoogle shortcode.Google Mobile is available through many wireless and mobile phone services worldwide.\n\nGoogle Local.Google Local enables users to find relevant local businesses near a city, postal code, or specific\naddress.This service combines Yellow Page listings with information found on web pages, and plots their\nlocations on interactive maps.Google Print.Google Print brings information online that had previously not been available to web\nsearchers.Under this program, we enable a number of publishers to host their content and show their\npublications at the top of our search results.","pageIdentifier":"17"},{"content":"Votes cast by important web pages with high PageRank weigh more heavily and are\nmore influential in deciding the PageRank of pages on the web.Text-Matching Techniques.Our technology employs text-matching techniques that compare search queries\nwith the content of web pages to help determine relevance.Our text-based scoring techniques do far more than\ncount the number of times a search term appears on a web page.For example, our technology determines the\nproximity of individual search terms to each other on a given web page, and prioritizes results that have the\nsearch terms near each other.Many other aspects of a page's content are factored into the equation, as is the\ncontent of pages that link to the page in question.By combining query independent measures such as PageRank\nwith our text-matching techniques, we are able to deliver search results that are relevant to what people are\ntrying to find.\n\nAdvertising Technology\nOur advertising program serves millions of relevant, targeted ads each day based on search terms people\n\nenter or content they view on the web.The key elements of our advertising technology include:\n\nGoogle AdWords Auction System.We use the Google AdWords auction system to enable advertisers to\nautomatically deliver relevant, targeted advertising.","pageIdentifier":"21"},{"content":"Votes cast by important web pages with high PageRank weigh more heavily and are\nmore influential in deciding the PageRank of pages on the web.Text-Matching Techniques.Our technology employs text-matching techniques that compare search queries\nwith the content of web pages to help determine relevance.Our text-based scoring techniques do far more than\ncount the number of times a search term appears on a web page.For example, our technology determines the\nproximity of individual search terms to each other on a given web page, and prioritizes results that have the\nsearch terms near each other.Many other aspects of a page's content are factored into the equation, as is the\ncontent of pages that link to the page in question.By combining query independent measures such as PageRank\nwith our text-matching techniques, we are able to deliver search results that are relevant to what people are\ntrying to find.\n\nAdvertising Technology\nOur advertising program serves millions of relevant, targeted ads each day based on search terms people\n\nenter or content they view on the web.The key elements of our advertising technology include:","pageIdentifier":"21"},{"content":"Google Maps—enables users to see maps, get directions, and find local businesses and services quickly\nand easily.Google Maps has several unique features, including draggable maps, integrated local search\nfrom Google Local, and keyboard shortcuts.Google Scholar—enables users to search specifically for scholarly literature, including peer-reviewed\npapers, theses, books, preprints, abstracts and technical reports from all broad areas of research.Google\nScholar can be used to find articles from a wide variety of academic publishers, professional societies,\npreprint repositories and universities, as well as scholarly articles available across the web.Google Suggest—guesses what you're typing and offers suggestions in real time.This is similar to\nGoogle's "Didyoumean?"feature that offers alternative spellings for your query after you search, except\nthat it works in real time.Google Video—includes thousands of programs that play on our TVs every day.Google Video enables\nyou to search a growing archive of televised content—everything from sports to dinosaur\ndocumentaries to news shows.\n\n6","pageIdentifier":"17"},{"content":"Every search query we process involves the automated\nexecution of an auction, resulting in our advertising system often processing hundreds of millions of auctions per\nday.To determine whether an ad is relevant to a particular query, this system weighs an advertiser's willingness\nto pay for prominence in the ad listings (the CPC) and interest from users in the ad as measured by the click\nthrough rate and other factors.If an ad does not attract user clicks, it moves to a less prominent position on the\npage, even if the advertiser offers to pay a high amount.This prevents advertisers with irrelevant ads from\n"squatting" in top positions to gain exposure.Conversely, more relevant, well-targeted ads that are clicked on\nfrequently move up in ranking, with no need for advertisers to increase their bids.Because we are paid only\nwhen users click on ads, the AdWords ranking system aligns our interests equally with those of our advertisers\nand our users.The more relevant and useful the ad, the better for our users, for our advertisers and for us.\n\nThe AdWords auction system also incorporates our AdWords discounter, which automatically lowers the\namount advertisers actually pay to the minimum needed to maintain their ad position.","pageIdentifier":"21"},{"content":"Web Search Technology\nOur web search technology uses a combination of techniques to determine the importance of a web page\nindependent of a particular search query and to determine the relevance of that page to a particular search\nquery.We do not explain how we do ranking in great detail because some people try to manipulate our search\nresults for their own gain, rather than in an attempt to provide high-quality information to users.\n\nRanking Technology.One element of our technology for ranking web pages is called PageRank.While we\ndeveloped much of our ranking technology after Google was formed, PageRank was developed at Stanford\nUniversity with the involvement of our founders, and was therefore published as research.Most of our current\nranking technology is protected as trade-secret.PageRank is a query-independent technique for determining the\nimportance of web pages by looking at the link structure of the web.PageRank treats a link from web page A to\nweb page B as a "vote" by page A in favor of page B.The PageRank of a page is the sum of the PageRank of the\npages that link to it.The PageRank of a web page also depends on the importance (or PageRank) of the other\nweb pages casting the votes.","pageIdentifier":"21"},{"content":"The Company recognizes as revenue the fees charged advertisers each time a user clicks on one of the text\nbased ads that are displayed next to the search results on Google web sites.Effective January 1, 2004, the\nCompany offered a single pricing structure to all of its advertisers based on the AdWords cost per click model.\n\nGoogle AdSense is the program through which the Company distributes its advertisers' text-based ads for\ndisplay on the web sites of the Google Network members.In accordance with Emerging Issues Task Force\n("EITF") Issue No. 99 19, Reporting Revenue Gross as a Principal Versus Net as an Agent, the Company recognizes\nas revenues the fees it receives from its advertisers.This revenue is reported gross primarily because the\nCompany is the primary obligor to its advertisers.\n\nThe Company generates fees from search services through a variety of contractual arrangements, which\ninclude per-query search fees and search service hosting fees.Revenues from set up and support fees and search\nservice hosting fees are recognized on a straight-line basis over the term of the contract, which is the expected\nperiod during which these services will be provided.The Company's policy is to recognize revenues from per\nquery search fees in the period queries are made and results are delivered.\n\nThe Company provides search services pursuant to certain AdSense agreements.","pageIdentifier":"85"},{"content":"On Google Print pages, we provide links to book sellers that may\noffer the full versions of these publications for sale, and we show content-targeted ads that are served through\nthe Google AdSense program.Google Desktop Search.Google Desktop Search enables our users to perform a full text search on the\ncontents of their own computer, including email, files, instant messenger chats and web browser history.Users\ncan use this service to view web pages they have visited even when they are not online.Google Alerts.Google Alerts are email updates of the latest relevant Google results (web, news, etc.) based\non the user's choice of query or topic.Typical uses include monitoring a developing news story, keeping current\non a competitor or industry, getting the latest on a celebrity or event, or keeping tabs on a favorite sports team.Google Labs.Google Labs is our playground for our engineers and for adventurous Google users.On Google\nLabs, we post product prototypes and solicit feedback on how the technology could be used or improved.Current Google Labs examples include:Google Personalized Search—provides customized search results based on an individual user's interests.Froogle Wireless—gives people the ability to search for product information from their mobile phones\nand other wireless devices.","pageIdentifier":"17"}]}]}}}

Get citations

Citations, when specified, are numbers that are placed inline in a searchsummary. These numbers indicate from which search results specific sentences inthe summary are taken.

To get citations, follow these steps:

1. Submit a search request that includescontentSearchSpec.summarySpecand specifies"includeCitations": true. For more information aboutsubmitting a search request, seeGet search results.

   In the following example,summarySpec indicates that you want a searchsummary, that the summary should be generated from the top three searchresults, and that citations should be included in the summary.

   "contentSearchSpec":{"summarySpec":{"summaryResultCount":3,"includeCitations":true},"extractiveContentSpec":{"maxExtractiveAnswerCount":1}}

   • summaryResultCount: The number of top results to generate the searchsummary from. If the number of results returned is less thansummaryResultCount, the summary is generated from all of the results.The maximum value is5.
   • includeCitations: A boolean that specifies whether citations arereturned.
   • maxExtractiveAnswerCount: The number of extractive answers to return foreach search result. The default value is 0 and the maximum is 1.

2. Get the summary, with citations, from the search response. Onesummaryproperty is returned in each response.

   Here is an example of a summary, with citations and citation metadata,returned at the end of a search response:

   "summary":{"summaryText":"BigQuery is Google Cloud's fully managed and completely  serverless enterprise data warehouse [1]. BigQuery supports all data types,  works across clouds, and has built-in machine learning and business  intelligence, all within a unified platform [2, 3].","summaryWithMetadata":{"summary":"BigQuery is Google Cloud's fully managed and completely   serverless enterprise data warehouse. BigQuery supports all data types,   works across clouds, and has built-in machine learning and business   intelligence, all within a unified platform.","citationMetadata":{"citations":[{"startIndex":"0","endIndex":"101","sources":[{"uri":"gs://example-dataset/html/6344007140738632642.html","title":"About BigQuery","id":"b6344007140738632642","referenceIndex":"0"},{"uri":"gs://example-dataset/html/1365490014946172719.html","title":"Google Cloud article","id":"b1365490014946172719","referenceIndex":"1"},{"uri":"gs://example-dataset/html/2687910668117268120.html","title":"BigQuery document","id":"a2687910668117268120","referenceIndex":"2"}]},{"startIndex":"103","endIndex":"230","sources":[{"referenceIndex":"0"},{"referenceIndex":"1"},{"referenceIndex":"2",}]}]},"references":[{"title":"Sports in the United States","docName":"projects/123/locations/global/collections/default_collection/dataStores/ds-123/branches/0/documents/b6344007140738632642","uri":"https://example.com/bigqueryA"},{"title":"Sports in the United States","docName":"projects/123/locations/global/collections/default_collection/dataStores/ds-123/branches/0/documents/b1365490014946172719","uri":"https://example.com/bigqueryB"},{"title":"Sports in the United States","docName":"projects/123/locations/global/collections/default_collection/dataStores/ds-123/branches/0/documents/a268791066811726812","uri":"https://example.com/bigqueryC"}]}}

   • summaryText: The search summary, with citation numbers. The citationnumbers refer to the returned search results and are 1-indexed. Forexample,[1] means that the sentence is attributed to the first searchresult.[2, 3] means that the sentence is attributed to both the secondand third search results.
   • citations: For each sentence in the summary that has a citation, liststhe metadata for that citation.
   • startIndex: Indicates the start of the sentence, measured inunicode bytes.
   • endIndex: Indicates the end of the sentence, measured inunicode bytes.
   • sources: Lists thereferenceIndex for each source that was includedin the sentence's citation.referenceIndex is the index number assignedto a source. The first source'sreferenceIndex isn't always explicitlyreturned in the response. BecausereferenceIndex is 0-indexed, the firstsource always has areferenceIndex of 0.
   • references: Lists metadata for each reference that was cited in thesummary. Metadata includestitle,docName, anduri.

Ignore adversarial queries

Adversarial queries include negative comments or are designed to generateunsafe, policy-violating output. You can specify that no search summaries shouldbe returned for adversarial queries. When an adversarial query is ignored, thesummaryText property contains boilerplate text indicating that no searchsummary is returned. Search documents are returned for adversarial queries eventhough search summaries are not.

To specify that no search summaries should be returned for adversarial queries,follow these steps:

1. Submit a search request that includescontentSearchSpec.summarySpecand specifies"ignoreAdversarialQuery": true. For more information aboutsubmitting a search request, seeGet search results.

   In the following example,summarySpec indicates that you want a searchsummary, that the summary should be generated from the top three searchresults, but that no summary should be returned for adversarial queries.

   "contentSearchSpec":{"summarySpec":{"summaryResultCount":3,"ignoreAdversarialQuery":true},"extractiveContentSpec":{"maxExtractiveAnswerCount":1}}

   • summaryResultCount: The number of top results to generate the searchsummary from. If the number of results returned is less thansummaryResultCount, the summary is generated from all of the results.The maximum value is5.
   • ignoreAdversarialQuery: A boolean that specifies that no searchsummaries should be returned for adversarial queries.
   • maxExtractiveAnswerCount: The number of extractive answers to return foreach search result. The default value is 0 and the maximum is 1.

2. See thesummary property that is returned for an adversarial searchrequest.

   Here is an example:

   "summary":{"summaryText":"We do not have a summary for your query. Here are some  search results.","summarySkippedReasons":["ADVERSARIAL_QUERY_IGNORED"]}

   • summaryText: Boilerplate text indicating that no search summary isreturned.
   • summarySkippedReasons: An enumeration with values for summary-skippedreasons.

Ignore non-summary seeking queries

Non-summary seeking queries return results that are not suitable forsummarization. For example, "why is the sky blue" and "Who is the best soccerplayer in the world?" are summary-seeking queries, but "SFO airport" and "worldcup 2026" are not. They are most likely navigational queries. You can specifythat no search summaries should be returned for non-summary seeking queries.Search documents are returned for non-summary seeking queries even though searchsummaries are not.

To specify that no search summaries should be returned for non-summary seekingqueries, follow these steps:

1. Submit a search request that includescontentSearchSpec.summarySpecand specifies"ignoreNonSummarySeekingQuery": true. For more informationabout submitting a search request, seeGet search results.

   In the following example,summarySpec indicates that you want a searchsummary, the summary should be generated from the top three search results,but that no summary should be returned for non-summary seeking queries.

   "contentSearchSpec":{"summarySpec":{"summaryResultCount":3,"ignoreNonSummarySeekingQuery":true},"extractiveContentSpec":{"maxExtractiveAnswerCount":1}}

   • summaryResultCount: The number of top results to generate the searchsummary from. If the number of results returned is less thansummaryResultCount, the summary is generated from all of the results.The maximum value is5.
   • ignoreNonSummarySeekingQuery: A boolean that specifies that no searchsummaries should be returned for non-summary seeking queries.
   • maxExtractiveAnswerCount: The number of extractive answers to return foreach search result. The default value is 0 and the maximum is 1.

2. See thesummary property that is returned for a non-summary seeking searchrequest.

   Here is an example:

   "summary":{"summaryText":"We do not have a summary for your query. Here are some  search results.","summarySkippedReasons":["NON_SUMMARY_SEEKING_QUERY_IGNORED"]}

   • summaryText: Boilerplate text indicating that no search summary isreturned.
   • summarySkippedReasons: An enumeration with values for summary-skippedreasons.

Get customized summaries

You can get customized summaries by providing natural-language instructions.This feature is available for the search widget by using the console, and forthe API by entering instructions in themodelPromptSpec.preamble field.

You can request customizations such as length, level of detail, style ofoutput (such as "simple"), language of output, focus of answer, and format (suchas tables, bullets, and XML).

For example, for the queryWhat are the benefits of bigquery?, you couldenter customization instructions like in the following examples:

• Example instruction:please show the answer format in an ordered list

  Example answer:

  The benefits of BigQuery are:1. Increased business productivity2. IT staff productivity3. Reduced infrastructure costs4. Loading and exporting data are free

• Example instruction:give a very detailed answer

  Example answer:

  BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse that enables businesses to analyze all their data very quickly. It is a fully managed service that takes care of provisioning, scaling, and managing the underlying infrastructure, so you can focus on analyzing your data. BigQuery is also integrated with other Google Cloud services, such as Google Kubernetes Engine, Cloud Data Fusion, and Cloud Dataproc, making it easy to build and deploy data pipelines.Here are some of the benefits of using BigQuery:* **Fast and scalable:** BigQuery can process petabytes of data very quickly, and it can scale to handle even the most demanding workloads. * **Cost-effective:** BigQuery is a very cost-effective way to store and analyze data. You only pay for the data that you use, and there are no upfront costs or commitments. * **Secure:** BigQuery is a secure platform that meets the needs of even the most security-conscious organizations. * **Easy to use:** BigQuery is easy to use, even for non-technical users. It has a simple and intuitive user interface, and it supports a variety of data sources. * **Integrated with other Google Cloud services:** BigQuery is integrated with other Google Cloud services, making it easy to build and deploy data pipelines.If you are looking for a fast, scalable, and cost-effective way to analyze your data, then BigQuery is a great option.

Best practices for customized summaries

If you plan to use this feature, do the following:

• Request only one customization at a time. Don't combinecustomizations—for example, requesting an HTML table in French.
• Google recommends that you impose limits on what customizations your end userscan request—for example, by offering a selector with a set of predefinedcustomizations.

Customize summaries

You can get customized summaries for only the search widget by using the consoleor, for any search request, by using the API.

To get a customized summary, follow these steps:

Specify the summarization model

You can specify the model that you want to use to generate summaries.

You can specifystable,preview, or a specific model version by name.For available model versions, seeAnswer generationmodel versions and lifecycle.

To change the model version:

1. Submit a search request that includesContentSearchSpec.SummarySpec.ModelSpec to specify the modelversion.

   "contentSearchSpec":{"summarySpec":{"modelSpec":{"version":"MODEL_VERSION"}}}

   • MODEL_VERSION: Specifies which model to use to generatesummaries. Supported values are:

     • stable: string. Default specification when no value is specified.stable points to a GA model version that's fine-tuned for answergeneration. Which modelstable points tochanges as new GA model versions are released and previous modelversions are discontinued. For the up-to-date version thatstablepoints to, seeAnswer generation model versions andlifecycle.

     • preview: string.preview points to the latestGemini model for question and answering. For moreinformation about Gemini, seeOverview ofmodels.

     • To specify a certain model version, enter the version name, suchasgemini-2.5-flash/answer_gen/v1. For supported versions, seeAnswer generation model versions andlifecycle.

For example, the following search request specifiespreview as the modelversion:

curl-XPOST\-H"Authorization: Bearer$(gcloudauthprint-access-token)"\-H"Content-Type: application/json"\"https://discoveryengine.googleapis.com/v1/projects/exampleproject/locations/global/collections/default_collection/dataStores/exampledatastore/servingConfigs/default_search:search"\-d'{  "query": "what is bigquery",  "contentSearchSpec": {    "summarySpec": {      "modelSpec": {        "version": "preview"      }    }  }}'

Limitations of search summaries

You might encounter the following limitations when usingsearch summaries:

• Because LLMs are used to generate searchsummaries and citations, the limitations of LLMs also apply toVertex AI Search summaries.

  For general information about these LLM limitations, seePaLM APIlimitationsin theVertex AI documentation.

• Search queries that require complex logical or analyticalreasoning or understanding of the world can lead to search summaries thatcontain incorrect information (hallucinations) or information that is notpresent in the unstructured or website data.

• Some statements in the search summary might not contain a citation:

  • If the system determines a statement doesn't require grounding, it won'tinclude a citation. Sentences like "Here is what I found" or "There aremany methods you can follow" lack citations.

  • Missing citations can also indicate that a valid reference wasn't found.Facts without citations might not be reliable.

• In rare cases, citations might be incorrectly attributed to astatement.

• Complex documents might be incorrectlyparsed by the LLM. In this case, the summary might be incomplete orincorrect.

• Because customization instructions are in natural language, adherence toinstructions can't be guaranteed for all requests.