Get snippets and extracted content

Agentspace Enterprise can provide search snippets, extractive answers, and extractive segments with each search response to enhance your results.

Snippets: A snippet is a brief extract of text from the search result document that provides a preview of a search result's content. It includes hit highlighting that you can render in your UI. Snippets are typically displayed under each search result to help end users assess the relevance and usefulness of that result. Snippets are available for data stores with unstructured data.
Extractive answers: An extractive answer is verbatim text that is returned with each search result. It is extracted directly from the original document. Extractive answers are typically displayed near the top of web pages to provide an end user with a brief answer that is contextually relevant to their query. Extractive answers are available for data stores with unstructured data.
Extractive segments: An extractive segment is verbatim text that is returned with each search result. An extractive segment is usually more verbose than an extractive answer. Extractive segments can be displayed as an answer to a query, and can be used to perform post-processing tasks and as input for large language models to generate answers or new text. Extractive segments are available for data stores with unstructured data.

Examples

The following examples help illustrate the differences between snippets, extractive answers, and extractive segments.

Query: "what is example product?"

Snippet:

To enable this, we are announcing our new Example Google Product, the fastest way for developers to jumpstart the creation of apps such as bots, ...
Extractive answer:

Example Google Product allows developers to quickly ship new experiences including bots, chat interfaces, custom search engines, digital assistants, and more. Developers have API access to Google's foundation models and can use out-of-the-box templates to jumpstart the creation of gen apps in minutes or hours.
Extractive segment:
Businesses and governments also want to make customer, partner, and employee interactions more effective and helpful with this new AI technology. To enable this, we are announcing our new Example Google Product.

Example Google Product allows developers to quickly ship new experiences including bots, chat interfaces, custom search engines, digital assistants, and more. Developers have API access to Google's foundation models and can use out-of-the-box templates to jumpstart the creation of gen apps in minutes or hours. With Example Google Product, developers will also:
- Combine organizational data and information retrieval techniques to provide relevant answers.
- Search and respond with more than just text.
- Combine natural conversations with structured flows.
- Don't just inform — transact.

Snippets

Snippets are short pieces of extracted verbatim from each search result document. They include hit highlighting in bold HTML tags for rendering previews of search results in a UI. Typically, snippets are rendered as preview text underneath a search result to help end users decide if clicking that search result will be useful.

Snippets are available for unstructured data.

Get snippets

To get snippets:

Send a search request that includes ContentSearchSpec.SnippetSpec and sets returnSnippet to true.

The following example of SnippetSpec specifies that a snippet can be returned for each search result.
```
"contentSearchSpec":
{
  "snippetSpec":
  {
    "returnSnippet": true
  }
}
```
- returnSnippet: If set to true, return a snippet.

Get snippets from the search response. Snippets are returned with each search result in derivedStructData.snippets.

In this example of a document that was returned as one of the results in a search response, a snippet with bold hit highlighting is included with the result:

{
  "id": "54321",
  "document": {
    "name": "projects/123/locations/global/collections/default_collection/dataStores/example-datastore/branches/0/documents/54321",
    "id": "54321",
    "derivedStructData": {
      "link": "gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs/2008_google_annual_report.pdf",
      "snippets": [
        {
          "snippet": "Google Chrome. Google Chrome is an open-source browser that combines a minimal design with technologies to make the web faster, safer, and easier to navigate.",
          "snippet_status": "SUCCESS"
        }
      ]
    }
  }
}

snippet: Contains a snippet generated for the document search result. Hit highlighting is included in bold HTML tags.
snippet_status: If a snippet is generated, this field is returned as SUCCESS. If no snippet is generated, this field is returned as NO_SNIPPET_AVAILABLE.

Extractive answers

An extractive answer is a section of text derived verbatim from a document. When a document is returned as a search result in a search response, a relevant extractive answer can be returned with that result.

An extractive answer can be text such as a paragraph, table, or bulleted list that is extracted from the search result document. Extractive answers are shorter than extractive segments.

Extractive answers can be used as an alternative to summarized responses in cases where precise, verbatim answers are preferable to rephrased summaries.

Extractive answers are available for data stores with unstructured data.

Get extractive answers

To get extractive answers:

Send a search request that uses ContentSearchSpec.extractiveContentSpec to specify maxExtractiveAnswerCount.

The following example of extractiveContentSpec specifies that an answer can be returned for each search result.
```
"contentSearchSpec":
{
  "extractiveContentSpec": {
    "maxExtractiveAnswerCount": 1
  }
}
```
- maxExtractiveAnswerCount: The number of extractive answers to return for each search result. The default value is 0 and the maximum is 5.

Get extractive answers from the search response. Extractive answers are returned with each search result in extractive_answers.

In this example of a document that was returned as one of the results in a search response, an extractive answer was included with the result:

{
  "id": "54321",
  "document": {
    "name": "projects/123/locations/global/collections/default_collection/dataStores/example-datastore/branches/0/documents/54321",
    "id": "54321",
    "derivedStructData": {
      "extractive_answers": [
        {
          "pageNumber": "2",
          "content": "Google saw growth throughout the year both in our domestic business and internationally, both on Google owned sites and on the Google Network. Specifically, revenues from Google owned sites increased 101% on a year over year basis, from $792 million to $1.6 billion."
        }
      ],
      "link": "gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs/2004Q4_earnings_google.pdf"
    }
}
}

pageNumber: If page numbers can be extracted from the document, this field indicates where the answer was extracted from.
content: The content of the extractive answer.

Extractive segments

An extractive segment is a section of text that is extracted verbatim from a search result document. Extractive segments are similar to extractive answers, but extractive segments usually more complete and verbose. Typically, extractive segments are used as input for your own LLMs to generate answers or new text.

Extractive segments can be multiple paragraphs, including formatted text such as tables and bulleted lists.

Extractive segments are available for data stores with unstructured data.

Extractive segment options

The following options are available for extractive segments:

Number of segments: You can specify up to 10 extractive segments to return for each search result.
Relevance scores: Relevance scores are based on the similarity of the query to the extracted segment. You can specify that extractive segments be returned with relevance scores. Scores range from -1.0 (less relevant) to 1.0 (more relevant). Turning on relevance scores can increase latency.

Note: The order in which results are returned is based on many factors. Although relevance scores are considered during ranking, they don't solely determine the ranking of results.
Adjacent segments: You can set numPreviousSegments and numNextSegments to get up to 3 segments from immediately before and after the relevant segment. Adjacent segments can add context and accuracy to the relevant segment.

Turning on adjacent segments can increase latency.

Get extractive segments

The following steps show how to get extractive segments for unstructured data.

Send a search request that uses ContentSearchSpec.extractiveContentSpec to specify maxExtractiveSegmentCount.

The following example of extractiveContentSpec specifies that one segment can be returned for each search result.
```
"contentSearchSpec":
{
  "extractiveContentSpec": {
    "maxExtractiveSegmentCount": 1
  }
}
```
- maxExtractiveSegmentCount: The number of extractive segments to return for each search result. The default value is 0 and the maximum is 10.
Additional options:
- returnExtractiveSegmentScore: Set to true to return a relevance score with each segment returned.
- numPreviousSegments: The number of adjacent segments to return before the relevant segment. The default value is 0 and the maximum is 3. Using adjacent segments can increase latency.
- numNextSegments: The number of adjacent segments to return after the relevant segment. The default value is 0 and the maximum is 3. Using adjacent segments can increase latency.
For more information about these options, see Extractive segment options.

Get segments from the search response. Segments are returned with each search result in extractive_segments.

In this example of a document that was returned as one of the results in a search response, a segment was included with the result:

{
  "id": "54321",
  "document": {
    "name": "projects/123/locations/global/collections/default_collection/dataStores/example-datastore/branches/0/documents/54321",
    "id": "54321",
    "derivedStructData": {
      "extractive_segments": [
        {
          "pageNumber": "2",
          "content": "Client\nGoogle Toolbar. Google Toolbar is a free application that adds a Google search box to web browsers (Internet\nExplorer and Firefox) and improves user web experience through features such as a pop-up blocker that blocks\npop-up advertising, an autofill feature that completes web forms with information saved on a user's computer, and\ncustomizable buttons that let users search their favorite web sites and stay updated on their favorite feeds.\n\nGoogle Chrome. Google Chrome is an open-source browser that combines a minimal design with\ntechnologies to make the web faster, safer, and easier to navigate.\nGoogle Pack. Google Pack is a free collection of safe, useful software programs from Google and other\ncompanies that improve the user experience online and on the desktop. It includes programs that help users\nbrowse the web faster, remove spyware and viruses.\n\nPicasa. Picasa is a free service that allows users to view, manage and share their photos. Picasa enables users\nto import, organize and edit their photos, and upload them to Picasa Web Albums where the photos can be shared\nwith others on the internet.\n\nGoogle Desktop. Google Desktop lets people perform a full-text search on the contents of their own\ncomputer, including email, files, instant messenger chats and web browser history. Users can view web pages they\nhave visited even when they are not online. Google Desktop also includes a customizable Sidebar that includes\nmodules for weather, stock tickers and news.\n\n5"
        }
      ],
      "link": "gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs/2004Q4_earnings_google.pdf"
    }
}
}

pageNumber: If page numbers can be extracted from the document, this field indicates where the answer was extracted from.
content: The content of the extractive segment.

What's next

Preview search results