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Grounding with your data

This page explains how you can ground responses by using your data from Vertex AI Search.

Grounding Gemini to your data

If you want to do retrieval-augmented generation (RAG), connect your model to your website data or your sets of documents, then use Grounding with Vertex AI Search.

Grounding to your data supports a maximum of 10 Vertex AI Search data sources and can be combined with Grounding with Google Search.

Supported models

This section lists the models that support grounding with your data.

Prerequisites

Before you can ground model output to your data, do the following:

  1. In the Google Cloud console, go to the IAM page, and search for the discoveryengine.servingConfigs.search permission, which is required for the grounding service to work.

Go to IAM 2. Enable AI Applications and activate the API. 3. Create a AI Applications data source and application.

See the Introduction to Vertex AI Search for more.

Enable AI Applications

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

AI Applications 2. Read and agree to the terms of service, then click Continue and activate the API.

Important: You must accept the discovery solutions data use terms for every project that you want to use AI Applications with.

AI Applications is available in the global location, or the eu and us multi-region. To learn more, see AI Applications locations

Create a data store in AI Applications

To create a data store in AI Applications, you can choose to ground with website data or documents.

Website

  1. Open the Create Data Store page from the Google Cloud console.
  2. In Website Content box, click Select. Specify the websites for your data store pane displays.
  3. If Advanced website indexing isn't checked, then select the Advanced website indexing checkbox to turn it on. Configure your data store pane displays.

  4. In the Specify URL patterns to index section, do the following:

  5. Add URLs for Sites to include.

  6. Optional: Add URLs for Sites to exclude.
  7. Click Continue.

  8. In the Configure your data store pane,

  9. Select a value from the Location of your data store list.

  10. Enter a name in the Your data store name field. The ID is generated. Use this ID when you generate your grounded responses with your data store. For more information, see Generate grounded responses with your data store.
  11. Click Create.

Documents

  1. Open the Create Data Store page from the Google Cloud console.
  2. In Cloud Storage box, click Select. Import data from Cloud Storage pane displays.
  3. In the Unstructured documents (PDF, HTML, TXT and more) section, select Unstructured documents (PDF, HTML, TXT and more).
  4. Select a Synchronization frequency option.
  5. Select a Select a folder or a file you want to import option, and enter the path in the field.
  6. Click Continue. Configure your data store pane displays.

  7. In the Configure your data store pane,

  8. Select a value from the Location of your data store list.

  9. Enter a name in the Your data store name field. The ID is generated.
  10. To select parsing and chunking options for your documents, expand the Document Processing Options section. For more information about different parsers, see Parse documents.
  11. Click Create.
  12. Click Create.

Generate grounded responses with your data store

Use the following instructions to ground a model with your data. A maximum of 10 data stores is supported.

If you don't know your data store ID, follow these steps:

  1. In the Google Cloud console, go to the AI Applications page and in the navigation menu, click Data stores.

Go to the Data stores page 2. Click the name of your data store. 3. On the Data page for your data store, get the data store ID.

Console

To ground your model output to AI Applications by using Vertex AI Studio in the Google Cloud console, follow these steps:

  1. In the Google Cloud console, go to the Vertex AI Studio Freeform page.

Go to Freeform 2. To turn on grounding, click the Grounding: your data toggle. 3. Click Customize. 1. Select Vertex AI Search as your source. 2. Using this path format, replace your data store's Project ID and the ID of the data store:

projects/project_id/locations/global/collections/default_collection/dataStores/data_store_id. 4. Click Save. 5. Enter your prompt in the text box, and click Submit.

Your prompt responses are grounded to AI Applications.

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google import genai
from google.genai.types import (
 GenerateContentConfig,
 HttpOptions,
 Retrieval,
 Tool,
 VertexAISearch,
)

client = genai.Client(http_options=HttpOptions(api_version="v1"))

# Load Data Store ID from Vertex AI Search
# datastore = "projects/111111111111/locations/global/collections/default_collection/dataStores/data-store-id"

response = client.models.generate_content(
 model="gemini-2.0-flash-001",
 contents="How do I make an appointment to renew my driver's license?",
 config=GenerateContentConfig(
 tools=[
 # Use Vertex AI Search Tool
 Tool(
 retrieval=Retrieval(
 vertex_ai_search=VertexAISearch(
 datastore=datastore,
 )
 )
 )
 ],
 ),
)

print(response.text)
# Example response:
# 'The process for making an appointment to renew your driver's license varies depending on your location. To provide you with the most accurate instructions...'

REST

To test a text prompt by using the Vertex AI API, send a POST request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

  • LOCATION: The region to process the request.
  • PROJECT_ID: Your project ID.
  • MODEL_ID: The model ID of the multimodal model.
  • TEXT: The text instructions to include in the prompt.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:generateContent

Request JSON body:

{
 "contents": [{
 "role": "user",
 "parts": [{
 "text": "TEXT"
 }]
 }],
 "tools": [{
 "retrieval": {
 "vertexAiSearch": {
 "datastore": projects/PROJECT_ID/locations/global/collections/default_collection/dataStores/DATA_STORE_ID
 }
 }
 }],
 "model": "projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID"
}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \ 
 -H "Authorization: Bearer $(gcloud auth print-access-token)" \ 
 -H "Content-Type: application/json; charset=utf-8" \ 
 -d @request.json \ 
 "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:generateContent"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token 
$headers = @{ "Authorization" = "Bearer $cred" } 

Invoke-WebRequest ` 
 -Method POST ` 
 -Headers $headers ` 
 -ContentType: "application/json; charset=utf-8" ` 
 -InFile request.json ` 
 -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:generateContent" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
 "candidates": [
 {
 "content": {
 "role": "model",
 "parts": [
 {
 "text": "You can make an appointment on the website https://dmv.gov/"
 }
 ]
 },
 "finishReason": "STOP",
 "safetyRatings": [
 "..."
 ],
 "groundingMetadata": {
 "retrievalQueries": [
 "How to make appointment to renew driving license?"
 ],
 "groundingChunks": [
 {
 "retrievedContext": {
 "uri": "https://vertexaisearch.cloud.google.com/grounding-api-redirect/AXiHM.....QTN92V5ePQ==",
 "title": "dmv"
 }
 }
 ],
 "groundingSupport": [
 {
 "segment": {
 "startIndex": 25,
 "endIndex": 147
 },
 "segment_text": "ipsum lorem ...",
 "supportChunkIndices": [1, 2],
 "confidenceScore": [0.9541752, 0.97726375]
 },
 {
 "segment": {
 "startIndex": 294,
 "endIndex": 439
 },
 "segment_text": "ipsum lorem ...",
 "supportChunkIndices": [1],
 "confidenceScore": [0.9541752, 0.9325467]
 }
 ]
 }
 }
 ],
 "usageMetadata": {
 "..."
 }
}

Understand your response

The response from both APIs include the LLM-generated text, which is called a candidate. If your model prompt successfully grounds to your Elasticsearch data source, then the responses include grounding metadata, which identifies the parts of the response that were derived from your Elasticsearch data. However, there are several reasons this metadata might not be provided, and the prompt response won't be grounded. These reasons include low-source relevance or incomplete information within the model's response.

The following is a breakdown of the output data:

  • Role: Indicates the sender of the grounded answer. Because the response always contains grounded text, the role is always model.
  • Text: The grounded answer generated by the LLM.
  • Grounding metadata: Information about the grounding source, which contains the following elements:
  • Grounding chunks: A list of results from your Elasticsearch index that support the answer.
  • Grounding supports: Information about a specific claim within the answer that can be used to show citations:
  • Segment: The part of the model's answer that is substantiated by a grounding chunk.
  • Grounding chunk index: The index of the grounding chunks in the grounding chunks list that corresponds to this claim.
  • Confidence scores: A number from 0 to 1 that indicates how grounded the claim is in the provided set of grounding chunks.

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