Ground Gemini model responses to Google Search

Use this to ground Gemini model output to Google Search results

Explore further

For detailed documentation that includes this code sample, see the following:

Code sample

C#

Before trying this sample, follow the C# setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI C# 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. 


using Google.Cloud.AIPlatform.V1;
using System;
using System.Threading.Tasks;

public class GroundingWebSample
{
    public async Task<string> GenerateTextWithGroundingWeb(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.5-flash-001"
    )
    {
        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = $"{location}-aiplatform.googleapis.com"
        }.Build();


        var generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
            GenerationConfig = new GenerationConfig
            {
                Temperature = 0.0f
            },
            Contents =
            {
                new Content
                {
                    Role = "USER",
                    Parts = { new Part { Text = "When is the next total solar eclipse in US?" } }
                }
            },
            Tools =
            {
                new Tool
                {
                    GoogleSearchRetrieval = new GoogleSearchRetrieval()
                }
            }
        };

        GenerateContentResponse response = await predictionServiceClient.GenerateContentAsync(generateContentRequest);

        string responseText = response.Candidates[0].Content.Parts[0].Text;
        Console.WriteLine(responseText);

        return responseText;
    }
}

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. 

import vertexai

from vertexai.generative_models import (
    GenerationConfig,
    GenerativeModel,
    Tool,
    grounding,
)

# TODO(developer): Update and un-comment below line
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

model = GenerativeModel("gemini-1.5-flash-001")

# Use Google Search for grounding
tool = Tool.from_google_search_retrieval(
    grounding.GoogleSearchRetrieval(
        # Optional: For Dynamic Retrieval
        dynamic_retrieval_config=grounding.DynamicRetrievalConfig(
            mode=grounding.DynamicRetrievalConfig.Mode.MODE_DYNAMIC,
            dynamic_threshold=0.7,
        )
    )
)

prompt = "When is the next total solar eclipse in US?"
response = model.generate_content(
    prompt,
    tools=[tool],
    generation_config=GenerationConfig(
        temperature=0.0,
    ),
)

print(response)
# Example response:
# The next total solar eclipse visible from the contiguous United States will be on **August 23, 2044**.

What's next

To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser.