Grounding with Google Search

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This page explains how to ground a model's responses using Google Search, which uses publicly-available web data.

Grounding with Google Search

If you want to connect your model with world knowledge, a wide possible range of topics, or up-to-date information on the Internet, then use Grounding with Google Search.

To learn more about model grounding in Vertex AI, see the Grounding overview.

Supported models

This section lists the models that support grounding with Search. To explore how each model generates grounded responses, follow these instructions:

1. Try a model listed in this table in the Google Cloud console.

2. Click the Grounding toggle to the on position.

Supported languages

For a listed of supported languages, see Languages.

Ground your model with Google Search

Use the following instructions to ground a model with publicly available web data.

Considerations

• To use grounding with Google Search, you must enable Google Search Suggestions. See more at Use Google Search suggestions.

• For ideal results, use a temperature of 0.0. To learn more about setting this config, see the Gemini API request body from the model reference.

• Grounding with Google Search has a limit of one million queries per day. If you require more queries, contact Google Cloud support for assistance.

pip install --upgrade google-genai

# 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=us-central1
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 ...'

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
}

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

{
  "contents": [{
    "role": "user",
    "parts": [{
      "text": "TEXT"
    }]
  }],
  "tools": [{
    "googleSearchRetrieval": {
      "dynamicRetrievalConfig": {
        "mode": "MODE_DYNAMIC",
        "dynamicThreshold": DYNAMIC_THRESHOLD
      }
    }
  }],
  "model": "projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID"
}

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"

$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

{
   "candidates": [
     {
       "content": {
         "role": "model",
         "parts": [
           {
             "text": "Chicago weather changes rapidly, so layers let you adjust easily. Consider a base layer, a warm mid-layer (sweater-fleece), and a weatherproof outer layer."
           }
         ]
       },
       "finishReason": "STOP",
       "safetyRatings":[
       "..."
    ],
       "groundingMetadata": {
         "webSearchQueries": [
           "What's the weather in Chicago this weekend?"
         ],
         "searchEntryPoint": {
            "renderedContent": "....................."
         }
         "groundingSupports": [
            {
              "segment": {
                "startIndex": 0,
                "endIndex": 65,
                "text": "Chicago weather changes rapidly, so layers let you adjust easily."
              },
              "groundingChunkIndices": [
                0
              ],
              "confidenceScores": [
                0.99
              ]
            },
          ]
          "retrievalMetadata": {
              "webDynamicRetrievalScore": 0.96879
            }
       }
     }
   ],
   "usageMetadata": { "..."
   }
 }

Understand your response

If your model prompt successfully grounds to Google Search from the Vertex AI Studio or from the API, then the responses include metadata with source links (web URLs). 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.

Grounding support

Displaying grounding support is recommended, because it aids you in validating responses from the publishers and adds avenues for further learning.

Grounding support for responses from Google Search sources should be shown both inline and in aggregate. For example, see the following image as a suggestion on how to do this.

Use of alternative search engine options

Customer's use of Grounding with Google Search does not prevent Customer from offering alternative search engine options, making alternative search options the default option for Customer Applications, or displaying their own or third party search suggestions or search results in Customer Applications, provided that that any such non-Google Search services or associated results are displayed separately from the Grounded Results and Search Suggestions and can't reasonably be attributed to, or confused with results provided by, Google.

Use Grounding with Google Search as a tool

Grounding with Google Search lets you provide the most current and accurate information to your model to improve the model's responses. Gemini 2.0 and later models can use Google Search as a tool in the following ways:

• The model can decide when to use Google Search.
• You can enable multi-turn searches and multi-tool queries like combining Grounding with Google Search and code execution.

The following example shows you how to configure Google Search as a tool.

  from google import genai
  from google.genai.types import Tool, GenerateContentConfig, GoogleSearch

  client = genai.Client()
  model_id = "gemini-2.0-flash-001"

  google_search_tool = Tool(
      google_search = GoogleSearch()
  )

  response = client.models.generate_content(
      model=model_id,
      contents="When is the next total solar eclipse in the United States?",
      config=GenerateContentConfig(
          tools=[google_search_tool],
          response_modalities=[text="TEXT"],
      )
)

for each in response.candidates[0].content.parts:
    print(each.text)
# Example response:
# The next total solar eclipse visible in the contiguous United States will be on ...

# To get grounding metadata as web content. This also includes Search Suggestions
print(response.candidates[0].grounding_metadata.search_entry_point.rendered_content)

Benefits

The following complex prompts and workflows that require planning, reasoning, and thinking can be done when you use Google Search as a tool:

• You can ground to help ensure responses are based on the latest and most accurate information.
• You can retrieve artifacts from the web to do analysis.
• You can find relevant images, videos, or other media to assist in multimodal reasoning or task generation.
• You can perform coding, technical troubleshooting, and other specialized tasks.
• You can find region-specific information, or assist in translating content accurately.
• You can find relevant websites for browsing.

What's next

• To learn more about grounding, see Grounding overview.
• To learn how to send chat prompt requests, see Multiturn chat.
• To learn about responsible AI best practices and Vertex AI's safety filters, see Safety best practices.