Check grounding with RAG

As part of your Retrieval Augmented Generation (RAG) experience in Vertex AI Agent Builder, you can check grounding to determine how grounded a piece of text (called an answer candidate) is in a given set of reference texts (called facts).

The check grounding API returns an overall support score of 0 to 1, which indicates how much the answer candidate agrees with the given facts. The response also includes citations to the facts supporting each claim in the answer candidate.

Perfect grounding requires that every claim in the answer candidate must be supported by one or more of the given facts. In other words, the claim is wholly entailed by the facts. If the claim is only partially entailed, it is not considered grounded. For example, the claim "Google was founded by Larry Page and Sergey Brin in 1975" is only partially correct—the names of the founders are correct but the date is wrong—and as such the whole claim is considered ungrounded. In this version of the check grounding API, a sentence is considered a single claim.

You can use the check grounding API to check any piece of text. It can be a human-generated blurb or a machine-generated response. A typical use case is to check an LLM-generated response against a given set of facts. The check grounding API is designed to be fast, with latency less than 500ms. This speed allows chat bots to call the check grounding API during each inference, without incurring a significant slowdown. The check grounding API can also provide references to support its findings, so that users can tell which parts of the generated response are reliable. The API also provides a support score to indicate the overall accuracy of the response. By setting a citation threshold, chat bots can filter out responses at inference time that are likely to contain hallucinated claims.

This page describes how to check grounding using the check grounding API.

Experimental features

If you want to try the following experimental features that are available for the check grounding API, contact your Google account team and ask to be added to the allowlist:

• Anti-citations: The anti-citations feature gives you a contradiction score that indicates how much the answer candidate contradicts the given facts. The response also includes citations to the contradicting facts for each claim. For more information, see Obtain a contradiction score for an answer candidate.

• Claim-level support score: A claim-level support score indicates the grounding of each claim in the answer candidate, in addition to the overall support score for the answer candidate. For more information, see Obtain claim-level scores for an answer candidate.

• Helpfulness score: The helpfulness score ia a measure of how well the answer candidate answers a given request. For more information, see Obtain a helpfulness score for an answer candidate.

• Grounding with a data store: Source the grounding facts from a Vertex AI Search data store instead of providing inline facts. For more information, see Check grounding with a data store.

Terms defined and explained

Before you use the check grounding API, it helps to understand the inputs and outputs, and how to structure your grounding facts for best results.

Input data

The check grounding API requires the following inputs in the request.

• Answer candidate: An answer candidate can be any piece of text whose grounding you want to check. For example, in the context of Vertex AI Search, the answer candidate might be the generated search summary that answers a query. The API would then determine how grounded the summary is in the input facts. An answer candidate can have a maximum length of 4096 tokens, where a token is defined as a word in a sentence or a period (a punctuation mark used to end the sentence). For example, the sentence "They wore off-the-rack clothes in 2024." is seven tokens long, including six words and a period.

• Facts: A set of text segments to be used as references for grounding. A set of metadata attributes (key-value pairs) can be supplied with each text segment. For example, "Author" and "Title" are typical attribute keys.

  The service supports up to 200 facts, each with a maximum of 10k characters.

  Google recommends against supplying one very large fact that contains all of the information. Instead, you can get better results by breaking large facts into smaller facts and supplying appropriate attributes for the smaller facts. For example, you can break up a large fact by title, author, or URL, and supply this information in attributes.

• Citation threshold: A float value from 0 to 1 that controls the confidence for the citations that support the answer candidate. A higher threshold imposes stricter confidence. Therefore, a higher threshold yields fewer but stronger citations.

Output data

The check grounding API returns the following for an answer candidate:

• Support score: The support score is a number from 0 to 1 that indicates how grounded an answer candidate is in the provided set of facts. It loosely approximates the fraction of claims in the answer candidate that were found to be grounded in one or more of the given facts.

• Cited chunks: Cited chunks are portions of the input facts that support the answer candidate.

• Claims and citations: The claims and citations connect a claim (typically a sentence) of the answer candidate to one or more of the cited chunks that corroborate the claim.

  When the claim-level score is enabled, with each claim, a support score is returned as a number from 0 to 1 that indicates how grounded the claim is in the provided set of facts. For more information, see Obtain claim-level scores for an answer candidate.

• Grounding check required: With each claim, a grounding-check-required boolean is returned. When this returns as False, it means that the system deems that the claim doesn't require grounding, and, therefore, citations and anti-citations aren't returned. For example, a sentence like "Here is what I found." isn't a fact by itself and, thus, doesn't require a grounding check.

  When the grounding-check-required returns as true, it means that a grounding check was performed and support scores, citations, and anti-citations, if any, are returned.

Obtain a support score for an answer candidate

To find out how grounded an answer candidate is in a set of facts, follow these steps:

1. Prepare your set of facts. For more information and examples, see Terms defined and explained.

2. Call the check method using the following code:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
-H "X-Goog-User-Project: PROJECT_ID" \
"https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID/locations/global/groundingConfigs/default_grounding_config:check" \
-d '{
  "answerCandidate": "CANDIDATE",
  "facts": [
  {
    "factText": "TEXT_0",
    "attributes": {"ATTRIBUTE_A": "VALUE_A0","ATTRIBUTE_B": "VALUE_B0"}
  },
  {
    "factText": "TEXT_1",
    "attributes": {"ATTRIBUTE_A": "VALUE_A1","ATTRIBUTE_B": "VALUE_B1"}
  },
  {
    "factText": "TEXT_2",
    "attributes": {"ATTRIBUTE_A": "VALUE_A2","ATTRIBUTE_B": "VALUE_B2"}
  }
  ],
  "groundingSpec": {
    "citationThreshold": "CITATION_THRESHOLD"
  }
}'

from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"

client = discoveryengine.GroundedGenerationServiceClient()

# The full resource name of the grounding config.
# Format: projects/{project_id}/locations/{location}/groundingConfigs/default_grounding_config
grounding_config = client.grounding_config_path(
    project=project_id,
    location="global",
    grounding_config="default_grounding_config",
)

request = discoveryengine.CheckGroundingRequest(
    grounding_config=grounding_config,
    answer_candidate="Titanic was directed by James Cameron. It was released in 1997.",
    facts=[
        discoveryengine.GroundingFact(
            fact_text=(
                "Titanic is a 1997 American epic romantic disaster movie. It was directed, written,"
                " and co-produced by James Cameron. The movie is about the 1912 sinking of the"
                " RMS Titanic. It stars Kate Winslet and Leonardo DiCaprio. The movie was released"
                " on December 19, 1997. It received positive critical reviews. The movie won 11 Academy"
                " Awards, and was nominated for fourteen total Academy Awards."
            ),
            attributes={"author": "Simple Wikipedia"},
        ),
        discoveryengine.GroundingFact(
            fact_text=(
                'James Cameron\'s "Titanic" is an epic, action-packed romance'
                "set against the ill-fated maiden voyage of the R.M.S. Titanic;"
                "the pride and joy of the White Star Line and, at the time,"
                "the largest moving object ever built. "
                'She was the most luxurious liner of her era -- the "ship of dreams" -- '
                "which ultimately carried over 1,500 people to their death in the "
                "ice cold waters of the North Atlantic in the early hours of April 15, 1912."
            ),
            attributes={"author": "Simple Wikipedia"},
        ),
    ],
    grounding_spec=discoveryengine.CheckGroundingSpec(citation_threshold=0.6),
)

response = client.check_grounding(request=request)

# Handle the response
print(response)

Examples of facts

The following are a couple of examples of facts and their attributes. These examples are to help you understand the grounding response and the format of the curl command.

• Fact 0

  • Text: "Titanic is a 1997 American epic romantic disaster movie. It was directed, written, and co-produced by James Cameron. The movie is about the 1912 sinking of the RMS Titanic. It stars Kate Winslet and Leonardo DiCaprio. The movie was released on December 19, 1997. It received positive critical reviews. The movie won 11 Academy Awards, and was nominated for fourteen total Academy Awards."

  • Attributes: {"Author": "Simple Wikipedia"}

• Fact 1

  • Text: "James Cameron's "Titanic" is an epic, action-packed romance set against the ill-fated maiden voyage of the R.M.S. Titanic; the pride and joy of the White Star Line and, at the time, the largest moving object ever built. She was the most luxurious liner of her era -- the "ship of dreams" -- which ultimately carried over 1,500 people to their death in the ice cold waters of the North Atlantic in the early hours of April 15, 1912."

  • Attributes: {"Author": "Rotten Tomatoes"}

Example request

After preparing the facts, you can send the following request, replacing the CANDIDATE field with different strings whose grounding you want to check.

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
    "https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID/locations/global/groundingConfigs/default_grounding_config:check" \
    -d '{
    "answerCandidate": "CANDIDATE",
    "facts": [
     {
      "factText": "Titanic is a 1997 American epic romantic disaster movie. It was directed, written, and co-produced by James Cameron. The movie is about the 1912 sinking of the RMS Titanic. It stars Kate Winslet and Leonardo DiCaprio. The movie was released on December 19, 1997. It received positive critical reviews. The movie won 11 Academy Awards, and was nominated for fourteen total Academy Awards.",
      "attributes": {"author":"Simple Wikipedia"}
     },
     {
      "factText": "James Cameron's \"Titanic\" is an epic, action-packed romance set against the ill-fated maiden voyage of the R.M.S. Titanic; the pride and joy of the White Star Line and, at the time, the largest moving object ever built. She was the most luxurious liner of her era -- the \"ship of dreams\" -- which ultimately carried over 1,500 people to their death in the ice cold waters of the North Atlantic in the early hours of April 15, 1912.",
      "attributes": {"author":"Simple Wikipedia"}
     }
    ],
    "groundingSpec": {
      "citationThreshold": "0.6"
    }
    }'

Examples of answer candidates and grounding responses

The following table shows examples of different answer candidates and responses when you send the example request, based on the example facts.

Obtain a contradiction score for an answer candidate

Along with the support score, you can also obtain a contradiction score. The contradiction score loosely approximates the fraction of claims that contradict the provided facts.

To try this Experimental feature, contact your Google account team and ask to be added to the allowlist.

Get a contradiction score

To obtain the contradiction score, follow these steps:

1. Prepare your set of facts. For more information and examples, see Terms defined and explained.

2. Call the check method, using the following curl command:

   curl -X POST \
   -H "Authorization: Bearer $(gcloud auth print-access-token)" \
   -H "Content-Type: application/json" \
   "https://discoveryengine.googleapis.com/v1alpha/projects/PROJECT_ID/locations/global/groundingConfigs/default_grounding_config:check" \
   -d '{
   "answerCandidate": "CANDIDATE",
   "facts": [
    {
     "factText": "TEXT_0",
     "attributes": {"ATTRIBUTE_A": "VALUE_A0","ATTRIBUTE_B": "VALUE_B0"}
    },
    {
     "factText": "TEXT_1",
     "attributes": {"ATTRIBUTE_A": "VALUE_A1","ATTRIBUTE_B": "VALUE_B1"}
    },
    {
     "factText": "TEXT_2",
     "attributes": {"ATTRIBUTE_A": "VALUE_A2","ATTRIBUTE_B": "VALUE_B2"}
    },
   ],
   "groundingSpec": {
     "citationThreshold": "CITATION_THRESHOLD",
     "enableAntiCitations": "ENABLE_ANTI_CITATION",
     "antiCitationThreshold": "ANTI_CITATION_THRESHOLD",
   }
   }'
   

   Replace the following:

   • PROJECT_ID: the project number or ID of your Google Cloud project.

   • CANDIDATE: the answer candidate string for which you want to get a support score—for example, Titanic was directed by James Cameron. It was released in 1997. An answer candidate can have a maximum length of 4096 tokens, where a token is defined as a word in a sentence or a period (a punctuation mark used to end the sentence). For example, the sentence "They wore off-the-rack clothes in 2024." is seven tokens long, including six words and a period.

   • TEXT: the text segment to be used for grounding—for example, Titanic is a 1997 American epic... Academy Awards. (See the full text in Examples of facts.)

   • ATTRIBUTE: the name of a metadata attribute associated with the fact—for example, author or title. It is a user-defined label to add more information to the fact text. For example, if the fact text Toronto is the capital of Ontario has an author attribute with its value as Wikipedia, then the following claims are well-grounded:

     • Wikipedia cites that Toronto is the capital of Ontario
     • Toronto is the capital of Ontario

     However, the claim that Government of Ontario claims that Toronto is the capital of Ontario is not as well-grounded.

   • VALUE: the value for the attribute—for example, Simple Wikipedia or Titanic (1997 film).

   • CITATION_THRESHOLD: a float value from 0 through 1 that determines whether a fact must be cited for a claim in the answer candidate. A higher threshold leads to fewer but strong citations to support the claim and a lower threshold leads to more but weak citations to support the claim. If unset, the default threshold value is 0.6.

   • ENABLE_ANTI_CITATION: a boolean value. Set this field to true to enable the experimental feature to evaluate the contradiction score. Either remove this field or set this field to false to turn off this feature.

   • ANTI_CITATION_THRESHOLD: a float value from 0 through 1 that determines whether a fact must be cited as contradicting a claim in the answer candidate. A higher threshold leads to fewer but stronger citations that contradict the claim, and a lower threshold leads to more but weaker citations that contradict the claim. If unset, the default threshold value is 0.8.

Example request

Using the example facts from the previous section, you can send the following request. Replace the CANDIDATE field with different strings whose grounding and contradictions you want to check.

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
    "https://discoveryengine.googleapis.com/v1alpha/projects/PROJECT_ID/locations/global/groundingConfigs/default_grounding_config:check" \
    -d '{
    "answerCandidate": "CANDIDATE",
    "facts": [
     {
      "factText": "Titanic is a 1997 American epic romantic disaster movie. It was directed, written, and co-produced by James Cameron. The movie is about the 1912 sinking of the RMS Titanic. It stars Kate Winslet and Leonardo DiCaprio. The movie was released on December 19, 1997. It received positive critical reviews. The movie won 11 Academy Awards, and was nominated for fourteen total Academy Awards.",
      "attributes": {"author":"Simple Wikipedia"}
     },
     {
      "factText": "James Cameron's \"Titanic\" is an epic, action-packed romance set against the ill-fated maiden voyage of the R.M.S. Titanic; the pride and joy of the White Star Line and, at the time, the largest moving object ever built. She was the most luxurious liner of her era -- the \"ship of dreams\" -- which ultimately carried over 1,500 people to their death in the ice cold waters of the North Atlantic in the early hours of April 15, 1912.",
      "attributes": {"author":"Simple Wikipedia"}
     }
    ],
    "groundingSpec": {
      "citationThreshold": "0.6",
      "enableAntiCitations": true,
      "antiCitationThreshold": "0.8",
    }
    }'

Example of responses with contradictions

The following table shows an example answer candidate and its response when you send the example request, based on the example facts.

Obtain a helpfulness score for an answer candidate

To try this Experimental feature, contact your Google account team and ask to be added to the allowlist.

In addition to the support score and the contradiction score, the check grounding API can provide a helpfulness score. A helpful response is one that effectively fulfills the user's request (as stated in the prompt) in an informative way. The helpfulness score is a measure of how well the response does the following:

• Addresses the core intent of the prompt
• Provides complete details while being concise
• Directly answers the question asked or completes the task requested in the prompt
• Offers relevant information
• Is clear and straightforward
• Avoids unnecessary details and jargon

To obtain a helpfulness score alongside the grounding score, you must provide a prompt together with the answer candidates and facts. The check grounding API reviews the answer candidate with the prompt and gives a score that indicates how helpfully the answer candidate answers the prompt. The score is in the range of [0,1] where the larger the score, the more helpful the answer.

Get a helpfulness score

To obtain the helpfulness score, follow these steps:

1. Prepare your prompt and answer candidate.

2. Call the check method, using the following curl command:

   curl -X POST \
   -H "Authorization: Bearer $(gcloud auth print-access-token)" \
   -H "Content-Type: application/json" \
   "https://discoveryengine.googleapis.com/v1alpha/projects/PROJECT_ID/locations/global/groundingConfigs/default_grounding_config:check" \
   -d '{
   "answerCandidate": "CANDIDATE",
   "facts": [
   {
     "factText": "TEXT_0",
     "attributes": {"ATTRIBUTE_A": "VALUE_A0","ATTRIBUTE_B": "VALUE_B0"}
   },
   {
     "factText": "TEXT_1",
     "attributes": {"ATTRIBUTE_A": "VALUE_A1","ATTRIBUTE_B": "VALUE_B1"}
   },
   {
     "factText": "TEXT_2",
     "attributes": {"ATTRIBUTE_A": "VALUE_A2","ATTRIBUTE_B": "VALUE_B2"}
   }
   ],
   "groundingSpec": {
     "enableHelpfulnessScore": true
   },
   "prompt": "PROMPT",
   }'
   

   Replace the following:

   • PROJECT_ID: the project number or ID of your Google Cloud project.

   • CANDIDATE: the answer candidate string for which you want to get a helpfulness score—for example, Titanic was directed by James Cameron. It was released in 1997. An answer candidate can have a maximum length of 4096 tokens.

   • TEXT: the text segment to be used for grounding—for example, Titanic is a 1997 American epic... Academy Awards. (See the full text in Examples of facts.)

   • ATTRIBUTE: the name of a metadata attribute associated with the fact—for example, author or title. It is a user-defined label to add more information to the fact text. For example, if the fact text Toronto is the capital of Ontario has an author attribute with its value as Wikipedia, then the following claims are well-grounded:

     • Wikipedia cites that Toronto is the capital of Ontario
     • Toronto is the capital of Ontario

     However, the claim that Government of Ontario claims that Toronto is the capital of Ontario is not as well-grounded.

   • VALUE: the value for the attribute—for example, Simple Wikipedia or Titanic (1997 film).

   • PROMPT: The prompt is the query that the answer candidate has been generated to answer—for example, Who directed and starred in the movie Titanic?.

Example request

Using the example facts from the previous section, you can send the following request. Replace the CANDIDATE field with different answer candidates to get a helpfulness score for the answer.

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
    "https://discoveryengine.googleapis.com/v1alpha/projects/PROJECT_ID/locations/global/groundingConfigs/default_grounding_config:check" \
    -d '{
    "answerCandidate": "CANDIDATE",
    "facts": [
     {
      "factText": "Titanic is a 1997 American epic romantic disaster movie. It was directed, written, and co-produced by James Cameron. The movie is about the 1912 sinking of the RMS Titanic. It stars Kate Winslet and Leonardo DiCaprio. The movie was released on December 19, 1997. It received positive critical reviews. The movie won 11 Academy Awards, and was nominated for fourteen total Academy Awards.",
      "attributes": {"author":"Simple Wikipedia"}
     },
     {
      "factText": "James Cameron's \"Titanic\" is an epic, action-packed romance set against the ill-fated maiden voyage of the R.M.S. Titanic; the pride and joy of the White Star Line and, at the time, the largest moving object ever built. She was the most luxurious liner of her era -- the \"ship of dreams\" -- which ultimately carried over 1,500 people to their death in the ice cold waters of the North Atlantic in the early hours of April 15, 1912.",
      "attributes": {"author":"Simple Wikipedia"}
     }
    ],
    "groundingSpec": {
      "enableHelpfulnessScore": true
    },
    "prompt": "Who directed and starred in the movie Titanic?"
    }'

Example of responses with helpfulness scores

The following table shows examples of answer candidates with their helpfulness scores. In each case, the prompt is Who directed and starred in the Titanic?

Obtain claim-level scores for an answer candidate

In addition to the answer-level support score, you can obtain a claim-level support score for each claim in an answer candidate.

To try this Experimental feature, contact your Google account team and ask to be added to the allowlist.

To obtain the claim-level scores, follow these steps:

1. Prepare your set of facts. For more information and examples, see Terms defined and explained.

2. Call the check method, using the following curl command:

   curl -X POST \
   -H "Authorization: Bearer $(gcloud auth print-access-token)" \
   -H "Content-Type: application/json" \
   "https://discoveryengine.googleapis.com/v1alpha/projects/PROJECT_ID/locations/global/groundingConfigs/default_grounding_config:check" \
   -d '{
   "answerCandidate": "CANDIDATE",
   "facts": [
    {
     "factText": "TEXT_0",
     "attributes": {"ATTRIBUTE_A": "VALUE_A0","ATTRIBUTE_B": "VALUE_B0"}
    },
    {
     "factText": "TEXT_1",
     "attributes": {"ATTRIBUTE_A": "VALUE_A1","ATTRIBUTE_B": "VALUE_B1"}
    },
    {
     "factText": "TEXT_2",
     "attributes": {"ATTRIBUTE_A": "VALUE_A2","ATTRIBUTE_B": "VALUE_B2"}
    },
   ],
   "groundingSpec": {
     "citationThreshold": "CITATION_THRESHOLD",
     "enableClaimLevelScore": "ENABLE_CLAIM_LEVEL_SCORE",
   }
   }'
   

   Replace the following:

   • PROJECT_ID: the project number or ID of your Google Cloud project.

   • CANDIDATE: the answer candidate string for which you want to get a support score—for example, Titanic was directed by James Cameron. It was released in 1997. An answer candidate can have a maximum length of 4096 tokens, where a token is defined as a word in a sentence or a period (a punctuation mark used to end the sentence). For example, the sentence "They wore off-the-rack clothes in 2024." is seven tokens long, including six words and a period.

   • TEXT: the text segment to be used for grounding—for example, Titanic is a 1997 American epic... Academy Awards. (See the full text in Examples of facts.)

   • ATTRIBUTE: the name of a metadata attribute associated with the fact—for example, author or title. It is a user-defined label to add more information to the fact text. For example, if the fact text Toronto is the capital of Ontario has an author attribute with its value as Wikipedia, then the following claims are well-grounded:

     • Wikipedia cites that Toronto is the capital of Ontario
     • Toronto is the capital of Ontario

     However, the claim that Government of Ontario claims that Toronto is the capital of Ontario is not as well-grounded.

   • VALUE: the value for the attribute—for example, Simple Wikipedia or Titanic (1997 film).

   • CITATION_THRESHOLD: a float value from 0 through 1 that determines whether a fact must be cited for a claim in the answer candidate. A higher threshold leads to fewer but strong citations to support the claim and a lower threshold leads to more but weak citations to support the claim. If unset, the default threshold value is 0.6.

   • ENABLE_CLAIM_LEVEL_SCORE: a boolean value. Set this field to true to enable the claim-level score feature. To turn off this feature, remove this field or set this field to false.

Example request

Using the example facts from the previous section, you can send the following request. Replace the CANDIDATE field with different strings whose per-claim grounding you want to check.

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
    "https://discoveryengine.googleapis.com/v1alpha/projects/PROJECT_ID/locations/global/groundingConfigs/default_grounding_config:check" \
    -d '{
    "answerCandidate": "CANDIDATE",
    "facts": [
     {
      "factText": "Titanic is a 1997 American epic romantic disaster movie. It was directed, written, and co-produced by James Cameron. The movie is about the 1912 sinking of the RMS Titanic. It stars Kate Winslet and Leonardo DiCaprio. The movie was released on December 19, 1997. It received positive critical reviews. The movie won 11 Academy Awards, and was nominated for fourteen total Academy Awards.",
      "attributes": {"author":"Simple Wikipedia"}
     },
     {
      "factText": "James Cameron's \"Titanic\" is an epic, action-packed romance set against the ill-fated maiden voyage of the R.M.S. Titanic; the pride and joy of the White Star Line and, at the time, the largest moving object ever built. She was the most luxurious liner of her era -- the \"ship of dreams\" -- which ultimately carried over 1,500 people to their death in the ice cold waters of the North Atlantic in the early hours of April 15, 1912.",
      "attributes": {"author":"Simple Wikipedia"}
     }
    ],
    "groundingSpec": {
      "citationThreshold": "0.6",
      "enableClaimLevelScore": true,
    }
    }'

Example of responses with claim-level scores

The following table shows an example answer candidate and its response when you send the example request, based on the example facts.

Check grounding with a data store

By default, grounding is checked against a set of facts that are provided inline in the check grounding API call. However, as an alternative to providing the facts inline, you can check the answer candidate against all the facts in a Vertex AI Search data store.

When you call the check grounding API, you provide the name of a Vertex AI search app. In turn, the unstructured data stores associated with that search app hold the set of facts that are used to check the grounding of the answer candidate.

To check grounding against a data store, follow these steps:

1. Identify a generic search app that's associated with at least one data store that contains unstructured data. The documents in this data store serve as the source for your grounding facts.

   For how to create a data store and search app, see Create a search data store and Create a search app.

2. 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 Agent Builder 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.

3. Call the check method, using the following curl command:

   curl -X POST \
   -H "Authorization: Bearer $(gcloud auth print-access-token)" \
   -H "Content-Type: application/json" \
   "https://discoveryengine.googleapis.com/v1alpha/projects/PROJECT_ID/locations/global/groundingConfigs/default_grounding_config:check" \
   -d '{
       "answerCandidate": "CANDIDATE",
       "groundingSource": {
         "searchSource": {
           "servingConfig": "projects/SOURCE_PROJECT_ID/locations/global/collections/default_collection/engines/APP_ID/servingConfigs/default_search"
         }
       }
   }'
   

   Replace the following:

   • PROJECT_ID: the project number or ID of your Google Cloud project.

   • SOURCE_PROJECT_ID: the project number or ID of the project that contains the app for grounding.

     This source project must be in the same region as your project—for example, both global or both eu. (For general information about multi-regions, see Location.)

   • CANDIDATE: the answer candidate string for which you want to get a support score—for example, Titanic was directed by James Cameron. It was released in 1997. An answer candidate can have a maximum length of 4096 tokens, where a token is defined as a word in a sentence or a period (a punctuation mark used to end the sentence). For example, the sentence "They wore off-the-rack clothes in 2024." is seven tokens long, including six words and a period.

   • APP_ID: the ID of the Vertex AI search app whose unstructured data stores contain the facts that you want to use as your grounding source.

   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/groundingConfigs/default_grounding_config:check" \
   -d '{
       "answerCandidate": "Titanic was directed by James Cameron. It was released in 1997.",
       "groundingSource": {
         "searchSource": {
           "servingConfig": "projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search"
         }
       }
   }'
   
   {
   "supportScore": 0.9884292,
   "claims": [
   {
     "startPos": 0,
     "endPos": 63,
     "claimText": "Titanic was directed by James Cameron. It was released in 1997.",
     "citationIndices": [
       0,
       1
     ],
     "groundingCheckRequired": true
   }
   ],
   "citedFacts": [
   {
     "chunkText": "James Cameron\'s \"Titanic\" is more than just a movie; it\'s a cinematic experience.  Released in 1997, the film masterfully intertwines a fictional love story with the historical tragedy of the RMS Titanic. Leonardo DiCaprio and Kate Winslet deliver captivating performances as Jack and Rose, two young lovers from different worlds who find each other amidst the grandeur of the doomed ship.",
     "sourceInfo": {
       "searchSource": {
         "documentId": "projects/123456/locations/global/collections/default_collection/dataStores/my-app/branches/0/documents/movie_reviews.pdf",
         "documentTitle": "movie_reviews",
         "documentLink": "gs://my-bucket-123/pdf/movie_reviews.pdf"
       }
     }
   },
   {
     "chunkText": "James Cameron\'s epic romance and disaster film \"Titanic,\" released in 1997, captured the hearts of audiences worldwide.  The film tells the fictional love story of Jack and Rose, two young people from different social classes who meet aboard the ill-fated RMS Titanic.  Cameron\'s masterful storytelling, combined with groundbreaking visual effects, brought the historical tragedy to life on the big screen, resulting in a cinematic phenomenon that resonated with viewers of all ages.",
     "sourceInfo": {
       "searchSource": {
         "documentId": "projects/123456/locations/global/collections/default_collection/dataStores/my-app/branches/0/documents/popular_films",
         "documentTitle": "popular_films",
         "documentLink": "gs://my-bucket-123/pdf/popular_films.pdf"
       }
     }
   }
   ]
   }
   

   The output fields are different from when the grounding source is given as facts inline. When a data store is used for grounding, cited facts are identified by document ID, title, and link from the data store; cited facts from a data store can't be identified by fact index.