Grounding bookmark_borderbookmark¶

Release Notes

In generative AI, grounding is the ability to connect model output to verifiable sources of information. If you provide models with access to specific data sources, then grounding tethers their output to these data and reduces the chances of inventing content.

With Vertex AI, you can ground model outputs in the following ways:

Ground with Google Search - ground a model with publicly available web data.
Ground to your own data - ground a model with your own data from Vertex AI Search as a data store (Preview).

For more information about grounding, see Grounding overview.

Supported models¶

Parameter list¶

See examples for implementation details.

`GoogleSearchRetrieval`¶

Ground the response with public data.

Parameters
`google_search_retrieval`	Required: `Object` Ground with publicly available web data.

`Retrieval`¶

Ground the response with private data from Vertex AI Search as a data store. Defines a retrieval tool that the model can call to access external knowledge.

Parameters
`source`	Required: `VertexAISearch` Ground with Vertex AI Search data sources.

`VertexAISearch`¶

Parameters
`datastore`	Required: `string` Fully-qualified data store resource ID from Vertex AI Search, in the following format: `projects/{project}/locations/{location}/collections/default_collection/dataStores/{datastore}`

Examples¶

Ground response on public web data using Google Search¶

Ground the response with Google Search public data. Include the google_search_retrieval tool in the request. No additional parameters are required.

Gen AI SDK for Python Gen AI SDK for Go More

Install¶

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

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

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

response = client.models.generate_content(
 model="gemini-2.0-flash-001",
 contents="When is the next total solar eclipse in the United States?",
 config=GenerateContentConfig(
 tools=[
 # Use Google Search Tool
 Tool(google_search=GoogleSearch())
 ],
 ),
)

print(response.text)
# Example response:
# 'The next total solar eclipse in the United States will occur on ...'

Learn how to install or update the Gen AI SDK for Go.

To learn more, see the SDK reference documentation.

Set environment variables to use the Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

import (
 "context"
 "fmt"
 "io"

 genai "google.golang.org/genai"
)

// generateWithGoogleSearch shows how to generate text using Google Search.
func generateWithGoogleSearch(w io.Writer) error {
 ctx := context.Background()

 client, err := genai.NewClient(ctx, &genai.ClientConfig{
 HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
 })
 if err != nil {
 return fmt.Errorf("failed to create genai client: %w", err)
 }

 modelName := "gemini-2.0-flash-001"
 contents := []*genai.Content{
 {Parts: []*genai.Part{
 {Text: "When is the next total solar eclipse in the United States?"},
 }},
 }
 config := &genai.GenerateContentConfig{
 Tools: []*genai.Tool{
 {GoogleSearch: &genai.GoogleSearch{}},
 },
 }

 resp, err := client.Models.GenerateContent(ctx, modelName, contents, config)
 if err != nil {
 return fmt.Errorf("failed to generate content: %w", err)
 }

 respText, err := resp.Text()
 if err != nil {
 return fmt.Errorf("failed to convert model response to text: %w", err)
 }
 fmt.Fprintln(w, respText)

 // Example response:
 // The next total solar eclipse in the United States will occur on March 30, 2033, but it will only ...

 return nil
}

Ground response on private data using Vertex AI Search¶

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Ground the response with data from a Vertex AI Search data store. For more information, see AI Applications.

Before you ground a response with private data, create a data store and a search app.

WARNING: For the time being, this "grounding" interface does not support Vertex AI Search "chunk mode".

Gen AI SDK for Python More

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...'

What's next¶

For detailed documentation, see the following:

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