Google অনুসন্ধানের সাথে গ্রাউন্ডিং

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(
    modelName: "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response.
    tools: [Tool.googleSearch()]
)

let response = try await model.generateContent("Who won the euro 2024?")
print(response.text ?? "No text in response.")

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response
    tools = listOf(Tool.googleSearch())
)

val response = model.generateContent("Who won the euro 2024?")
print(response.text)

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel("GEMINI_MODEL_NAME",
                        null,
                        null,
                        // Provide Google Search as a tool that the model can use to generate its response
                        List.of(Tool.GoogleSearch()));

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);

ListenableFuture response = model.generateContent("Who won the euro 2024?");
  Futures.addCallback(response, new FutureCallback() {
      @Override
      public void onSuccess(GenerateContentResponse result) {
          String resultText = result.getText();
          System.out.println(resultText);
      }

      @Override
      public void onFailure(Throwable t) {
          t.printStackTrace();
      }
  }, executor);

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer): Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(
  ai,
  {
    model: "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response
    tools: [{ googleSearch: {} }]
  }
);

const result = await model.generateContent("Who won the euro 2024?");

console.log(result.response.text());

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_ai/firebase_ai.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp.
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_MODEL_NAME',
  // Provide Google Search as a tool that the model can use to generate its response.
  tools: [
    Tool.googleSearch(),
  ],
);

final response = await model.generateContent([Content.text("Who won the euro 2024?")]);
print(response.text);

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide Google Search as a tool that the model can use to generate its response.
  tools: new[] { new Tool(new GoogleSearch()) }
);

var response = await model.GenerateContentAsync("Who won the euro 2024?");
UnityEngine.Debug.Log(response.Text ?? "No text in response.");

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// ...

// Get the model's response
let text = response.text

// Get the grounding metadata
if let candidate = response.candidates.first,
   let groundingMetadata = candidate.groundingMetadata {
  // REQUIRED - display Google Search suggestions
  // (renderedContent contains HTML and CSS for the search widget)
  if let renderedContent = groundingMetadata.searchEntryPoint?.renderedContent {
    // TODO(developer): Display Google Search suggestions using a WebView
  }

  // REQUIRED - display sources
  let groundingChunks = groundingMetadata.groundingChunks
  for chunk in groundingMetadata.groundingChunks {
    if let web = chunk.web {
      let title = web.title  // for example, "uefa.com"
      let uri = web.uri  // for example, "https://vertexaisearch.cloud.google.com..."
      // TODO(developer): show source in the UI
    }
  }
}

// ...

// Get the model's response
val text = response.text

// Get the grounding metadata
val groundingMetadata = response.candidates.firstOrNull()?.groundingMetadata

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
val renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent
if (renderedContent != null) {
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
val groundingChunks = groundingMetadata?.groundingChunks
groundingChunks?.let { chunks ->
  for (chunk in chunks) {
  	val title = chunk.web?.title  // for example, "uefa.com"
	val uri = chunk.web?.uri  // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show source in the UI
  }
}

// ...

Futures.addCallback(response, new FutureCallback() {
  @Override
  public void onSuccess(GenerateContentResponse result) {
  // Get the model's response
  String text = result.getText();

  // Get the grounding metadata
  GroundingMetadata groundingMetadata =
  result.getCandidates()[0].getGroundingMetadata();

  if (groundingMetadata != null) {
    // REQUIRED - display Google Search suggestions
  // (renderedContent contains HTML and CSS for the search widget)
    String renderedContent =
  groundingMetadata.getSearchEntryPoint().getRenderedContent();
    if (renderedContent != null) {
      // TODO(developer): Display Google Search suggestions using a WebView
    }

    // REQUIRED - display sources
    List chunks = groundingMetadata.getGroundingChunks();
    if (chunks != null) {
      for(GroundingChunk chunk : chunks) {
        WebGroundingChunk web = chunk.getWeb();
        if (web != null) {
          String title = web.getTitle();  // for example, "uefa.com"
          String uri = web.getUri();  // for example, "https://vertexaisearch.cloud.google.com..."
          // TODO(developer): show sources in the UI
        }
      }
    }
  }
  }

  @Override
  public void onFailure(Throwable t) {
  t.printStackTrace();
  }
  }, executor);

// ...

// Get the model's text response
const text = result.response.text();

// Get the grounding metadata
const groundingMetadata = result.response.candidates?.[0]?.groundingMetadata;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
const renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent) {
  // TODO(developer): render this HTML and CSS in the UI
}

// REQUIRED - display sources
const groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks) {
  for (const chunk of groundingChunks) {
    const title = chunk.web?.title;  // for example, "uefa.com"
    const uri = chunk.web?.uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
  }
}

// ...

// Get the model's response
final text = response.text;

// Get the grounding metadata
final groundingMetadata = response.candidates.first.groundingMetadata;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
final renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent != null) {
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
final groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks != null) {
  for (var chunk in groundingChunks) {
    final title = chunk.web?.title;  // for example, "uefa.com"
    final uri = chunk.web?.uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
  }
}

// ...

// Get the model's response.
var text = response.Text;

// Get the grounding metadata.
var groundingMetadata = response.Candidates.First().GroundingMetadata.Value;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
if (groundingMetadata.SearchEntryPoint.HasValue) {
    var renderedContent = groundingMetadata.SearchEntryPoint.Value.RenderedContent;
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
foreach(GroundingChunk chunk in groundingMetadata.GroundingChunks) {
    var title = chunk.Web.Value.Title;  // for example, "uefa.com"
    var uri = chunk.Web.Value.Uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
}

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(
    modelName: "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response.
    tools: [Tool.googleSearch()]
)

let response = try await model.generateContent("Who won the euro 2024?")
print(response.text ?? "No text in response.")

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response
    tools = listOf(Tool.googleSearch())
)

val response = model.generateContent("Who won the euro 2024?")
print(response.text)

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel("GEMINI_MODEL_NAME",
                        null,
                        null,
                        // Provide Google Search as a tool that the model can use to generate its response
                        List.of(Tool.GoogleSearch()));

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);

ListenableFuture response = model.generateContent("Who won the euro 2024?");
  Futures.addCallback(response, new FutureCallback() {
      @Override
      public void onSuccess(GenerateContentResponse result) {
          String resultText = result.getText();
          System.out.println(resultText);
      }

      @Override
      public void onFailure(Throwable t) {
          t.printStackTrace();
      }
  }, executor);

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer): Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(
  ai,
  {
    model: "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response
    tools: [{ googleSearch: {} }]
  }
);

const result = await model.generateContent("Who won the euro 2024?");

console.log(result.response.text());

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_ai/firebase_ai.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp.
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_MODEL_NAME',
  // Provide Google Search as a tool that the model can use to generate its response.
  tools: [
    Tool.googleSearch(),
  ],
);

final response = await model.generateContent([Content.text("Who won the euro 2024?")]);
print(response.text);

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide Google Search as a tool that the model can use to generate its response.
  tools: new[] { new Tool(new GoogleSearch()) }
);

var response = await model.GenerateContentAsync("Who won the euro 2024?");
UnityEngine.Debug.Log(response.Text ?? "No text in response.");

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// ...

// Get the model's response
let text = response.text

// Get the grounding metadata
if let candidate = response.candidates.first,
   let groundingMetadata = candidate.groundingMetadata {
  // REQUIRED - display Google Search suggestions
  // (renderedContent contains HTML and CSS for the search widget)
  if let renderedContent = groundingMetadata.searchEntryPoint?.renderedContent {
    // TODO(developer): Display Google Search suggestions using a WebView
  }

  // REQUIRED - display sources
  let groundingChunks = groundingMetadata.groundingChunks
  for chunk in groundingMetadata.groundingChunks {
    if let web = chunk.web {
      let title = web.title  // for example, "uefa.com"
      let uri = web.uri  // for example, "https://vertexaisearch.cloud.google.com..."
      // TODO(developer): show source in the UI
    }
  }
}

// ...

// Get the model's response
val text = response.text

// Get the grounding metadata
val groundingMetadata = response.candidates.firstOrNull()?.groundingMetadata

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
val renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent
if (renderedContent != null) {
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
val groundingChunks = groundingMetadata?.groundingChunks
groundingChunks?.let { chunks ->
  for (chunk in chunks) {
  	val title = chunk.web?.title  // for example, "uefa.com"
	val uri = chunk.web?.uri  // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show source in the UI
  }
}

// ...

Futures.addCallback(response, new FutureCallback() {
  @Override
  public void onSuccess(GenerateContentResponse result) {
  // Get the model's response
  String text = result.getText();

  // Get the grounding metadata
  GroundingMetadata groundingMetadata =
  result.getCandidates()[0].getGroundingMetadata();

  if (groundingMetadata != null) {
    // REQUIRED - display Google Search suggestions
  // (renderedContent contains HTML and CSS for the search widget)
    String renderedContent =
  groundingMetadata.getSearchEntryPoint().getRenderedContent();
    if (renderedContent != null) {
      // TODO(developer): Display Google Search suggestions using a WebView
    }

    // REQUIRED - display sources
    List chunks = groundingMetadata.getGroundingChunks();
    if (chunks != null) {
      for(GroundingChunk chunk : chunks) {
        WebGroundingChunk web = chunk.getWeb();
        if (web != null) {
          String title = web.getTitle();  // for example, "uefa.com"
          String uri = web.getUri();  // for example, "https://vertexaisearch.cloud.google.com..."
          // TODO(developer): show sources in the UI
        }
      }
    }
  }
  }

  @Override
  public void onFailure(Throwable t) {
  t.printStackTrace();
  }
  }, executor);

// ...

// Get the model's text response
const text = result.response.text();

// Get the grounding metadata
const groundingMetadata = result.response.candidates?.[0]?.groundingMetadata;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
const renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent) {
  // TODO(developer): render this HTML and CSS in the UI
}

// REQUIRED - display sources
const groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks) {
  for (const chunk of groundingChunks) {
    const title = chunk.web?.title;  // for example, "uefa.com"
    const uri = chunk.web?.uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
  }
}

// ...

// Get the model's response
final text = response.text;

// Get the grounding metadata
final groundingMetadata = response.candidates.first.groundingMetadata;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
final renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent != null) {
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
final groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks != null) {
  for (var chunk in groundingChunks) {
    final title = chunk.web?.title;  // for example, "uefa.com"
    final uri = chunk.web?.uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
  }
}

// ...

// Get the model's response.
var text = response.Text;

// Get the grounding metadata.
var groundingMetadata = response.Candidates.First().GroundingMetadata.Value;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
if (groundingMetadata.SearchEntryPoint.HasValue) {
    var renderedContent = groundingMetadata.SearchEntryPoint.Value.RenderedContent;
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
foreach(GroundingChunk chunk in groundingMetadata.GroundingChunks) {
    var title = chunk.Web.Value.Title;  // for example, "uefa.com"
    var uri = chunk.Web.Value.Uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
}

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(
    modelName: "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response.
    tools: [Tool.googleSearch()]
)

let response = try await model.generateContent("Who won the euro 2024?")
print(response.text ?? "No text in response.")

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response
    tools = listOf(Tool.googleSearch())
)

val response = model.generateContent("Who won the euro 2024?")
print(response.text)

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel("GEMINI_MODEL_NAME",
                        null,
                        null,
                        // Provide Google Search as a tool that the model can use to generate its response
                        List.of(Tool.GoogleSearch()));

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);

ListenableFuture response = model.generateContent("Who won the euro 2024?");
  Futures.addCallback(response, new FutureCallback() {
      @Override
      public void onSuccess(GenerateContentResponse result) {
          String resultText = result.getText();
          System.out.println(resultText);
      }

      @Override
      public void onFailure(Throwable t) {
          t.printStackTrace();
      }
  }, executor);

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer): Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(
  ai,
  {
    model: "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response
    tools: [{ googleSearch: {} }]
  }
);

const result = await model.generateContent("Who won the euro 2024?");

console.log(result.response.text());

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_ai/firebase_ai.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp.
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_MODEL_NAME',
  // Provide Google Search as a tool that the model can use to generate its response.
  tools: [
    Tool.googleSearch(),
  ],
);

final response = await model.generateContent([Content.text("Who won the euro 2024?")]);
print(response.text);

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide Google Search as a tool that the model can use to generate its response.
  tools: new[] { new Tool(new GoogleSearch()) }
);

var response = await model.GenerateContentAsync("Who won the euro 2024?");
UnityEngine.Debug.Log(response.Text ?? "No text in response.");

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// ...

// Get the model's response
let text = response.text

// Get the grounding metadata
if let candidate = response.candidates.first,
   let groundingMetadata = candidate.groundingMetadata {
  // REQUIRED - display Google Search suggestions
  // (renderedContent contains HTML and CSS for the search widget)
  if let renderedContent = groundingMetadata.searchEntryPoint?.renderedContent {
    // TODO(developer): Display Google Search suggestions using a WebView
  }

  // REQUIRED - display sources
  let groundingChunks = groundingMetadata.groundingChunks
  for chunk in groundingMetadata.groundingChunks {
    if let web = chunk.web {
      let title = web.title  // for example, "uefa.com"
      let uri = web.uri  // for example, "https://vertexaisearch.cloud.google.com..."
      // TODO(developer): show source in the UI
    }
  }
}

// ...

// Get the model's response
val text = response.text

// Get the grounding metadata
val groundingMetadata = response.candidates.firstOrNull()?.groundingMetadata

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
val renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent
if (renderedContent != null) {
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
val groundingChunks = groundingMetadata?.groundingChunks
groundingChunks?.let { chunks ->
  for (chunk in chunks) {
  	val title = chunk.web?.title  // for example, "uefa.com"
	val uri = chunk.web?.uri  // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show source in the UI
  }
}

// ...

Futures.addCallback(response, new FutureCallback() {
  @Override
  public void onSuccess(GenerateContentResponse result) {
  // Get the model's response
  String text = result.getText();

  // Get the grounding metadata
  GroundingMetadata groundingMetadata =
  result.getCandidates()[0].getGroundingMetadata();

  if (groundingMetadata != null) {
    // REQUIRED - display Google Search suggestions
  // (renderedContent contains HTML and CSS for the search widget)
    String renderedContent =
  groundingMetadata.getSearchEntryPoint().getRenderedContent();
    if (renderedContent != null) {
      // TODO(developer): Display Google Search suggestions using a WebView
    }

    // REQUIRED - display sources
    List chunks = groundingMetadata.getGroundingChunks();
    if (chunks != null) {
      for(GroundingChunk chunk : chunks) {
        WebGroundingChunk web = chunk.getWeb();
        if (web != null) {
          String title = web.getTitle();  // for example, "uefa.com"
          String uri = web.getUri();  // for example, "https://vertexaisearch.cloud.google.com..."
          // TODO(developer): show sources in the UI
        }
      }
    }
  }
  }

  @Override
  public void onFailure(Throwable t) {
  t.printStackTrace();
  }
  }, executor);

// ...

// Get the model's text response
const text = result.response.text();

// Get the grounding metadata
const groundingMetadata = result.response.candidates?.[0]?.groundingMetadata;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
const renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent) {
  // TODO(developer): render this HTML and CSS in the UI
}

// REQUIRED - display sources
const groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks) {
  for (const chunk of groundingChunks) {
    const title = chunk.web?.title;  // for example, "uefa.com"
    const uri = chunk.web?.uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
  }
}

// ...

// Get the model's response
final text = response.text;

// Get the grounding metadata
final groundingMetadata = response.candidates.first.groundingMetadata;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
final renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent != null) {
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
final groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks != null) {
  for (var chunk in groundingChunks) {
    final title = chunk.web?.title;  // for example, "uefa.com"
    final uri = chunk.web?.uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
  }
}

// ...

// Get the model's response.
var text = response.Text;

// Get the grounding metadata.
var groundingMetadata = response.Candidates.First().GroundingMetadata.Value;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
if (groundingMetadata.SearchEntryPoint.HasValue) {
    var renderedContent = groundingMetadata.SearchEntryPoint.Value.RenderedContent;
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
foreach(GroundingChunk chunk in groundingMetadata.GroundingChunks) {
    var title = chunk.Web.Value.Title;  // for example, "uefa.com"
    var uri = chunk.Web.Value.Uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
}

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(
    modelName: "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response.
    tools: [Tool.googleSearch()]
)

let response = try await model.generateContent("Who won the euro 2024?")
print(response.text ?? "No text in response.")

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response
    tools = listOf(Tool.googleSearch())
)

val response = model.generateContent("Who won the euro 2024?")
print(response.text)

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
                .generativeModel("GEMINI_MODEL_NAME",
                        null,
                        null,
                        // Provide Google Search as a tool that the model can use to generate its response
                        List.of(Tool.GoogleSearch()));

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);

ListenableFuture response = model.generateContent("Who won the euro 2024?");
  Futures.addCallback(response, new FutureCallback() {
      @Override
      public void onSuccess(GenerateContentResponse result) {
          String resultText = result.getText();
          System.out.println(resultText);
      }

      @Override
      public void onFailure(Throwable t) {
          t.printStackTrace();
      }
  }, executor);

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer): Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(
  ai,
  {
    model: "GEMINI_MODEL_NAME",
    // Provide Google Search as a tool that the model can use to generate its response
    tools: [{ googleSearch: {} }]
  }
);

const result = await model.generateContent("Who won the euro 2024?");

console.log(result.response.text());

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_ai/firebase_ai.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp.
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'GEMINI_MODEL_NAME',
  // Provide Google Search as a tool that the model can use to generate its response.
  tools: [
    Tool.googleSearch(),
  ],
);

final response = await model.generateContent([Content.text("Who won the euro 2024?")]);
print(response.text);

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(
  modelName: "GEMINI_MODEL_NAME",
  // Provide Google Search as a tool that the model can use to generate its response.
  tools: new[] { new Tool(new GoogleSearch()) }
);

var response = await model.GenerateContentAsync("Who won the euro 2024?");
UnityEngine.Debug.Log(response.Text ?? "No text in response.");

// Make sure to comply with the "Grounding with Google Search" usage requirements,
// which includes how you use and display the grounded result

// ...

// Get the model's response
let text = response.text

// Get the grounding metadata
if let candidate = response.candidates.first,
   let groundingMetadata = candidate.groundingMetadata {
  // REQUIRED - display Google Search suggestions
  // (renderedContent contains HTML and CSS for the search widget)
  if let renderedContent = groundingMetadata.searchEntryPoint?.renderedContent {
    // TODO(developer): Display Google Search suggestions using a WebView
  }

  // REQUIRED - display sources
  let groundingChunks = groundingMetadata.groundingChunks
  for chunk in groundingMetadata.groundingChunks {
    if let web = chunk.web {
      let title = web.title  // for example, "uefa.com"
      let uri = web.uri  // for example, "https://vertexaisearch.cloud.google.com..."
      // TODO(developer): show source in the UI
    }
  }
}

// ...

// Get the model's response
val text = response.text

// Get the grounding metadata
val groundingMetadata = response.candidates.firstOrNull()?.groundingMetadata

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
val renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent
if (renderedContent != null) {
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
val groundingChunks = groundingMetadata?.groundingChunks
groundingChunks?.let { chunks ->
  for (chunk in chunks) {
  	val title = chunk.web?.title  // for example, "uefa.com"
	val uri = chunk.web?.uri  // for example, "https://vertexaisearch.cloud.google.com..."
// TODO(developer): show source in the UI
  }
}

// ...

Futures.addCallback(response, new FutureCallback() {
  @Override
  public void onSuccess(GenerateContentResponse result) {
  // Get the model's response
  String text = result.getText();

  // Get the grounding metadata
  GroundingMetadata groundingMetadata =
  result.getCandidates()[0].getGroundingMetadata();

  if (groundingMetadata != null) {
    // REQUIRED - display Google Search suggestions
  // (renderedContent contains HTML and CSS for the search widget)
    String renderedContent =
  groundingMetadata.getSearchEntryPoint().getRenderedContent();
    if (renderedContent != null) {
      // TODO(developer): Display Google Search suggestions using a WebView
    }

    // REQUIRED - display sources
    List chunks = groundingMetadata.getGroundingChunks();
    if (chunks != null) {
      for(GroundingChunk chunk : chunks) {
        WebGroundingChunk web = chunk.getWeb();
        if (web != null) {
          String title = web.getTitle();  // for example, "uefa.com"
          String uri = web.getUri();  // for example, "https://vertexaisearch.cloud.google.com..."
          // TODO(developer): show sources in the UI
        }
      }
    }
  }
  }

  @Override
  public void onFailure(Throwable t) {
  t.printStackTrace();
  }
  }, executor);

// ...

// Get the model's text response
const text = result.response.text();

// Get the grounding metadata
const groundingMetadata = result.response.candidates?.[0]?.groundingMetadata;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
const renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent) {
  // TODO(developer): render this HTML and CSS in the UI
}

// REQUIRED - display sources
const groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks) {
  for (const chunk of groundingChunks) {
    const title = chunk.web?.title;  // for example, "uefa.com"
    const uri = chunk.web?.uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
  }
}

// ...

// Get the model's response
final text = response.text;

// Get the grounding metadata
final groundingMetadata = response.candidates.first.groundingMetadata;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
final renderedContent = groundingMetadata?.searchEntryPoint?.renderedContent;
if (renderedContent != null) {
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
final groundingChunks = groundingMetadata?.groundingChunks;
if (groundingChunks != null) {
  for (var chunk in groundingChunks) {
    final title = chunk.web?.title;  // for example, "uefa.com"
    final uri = chunk.web?.uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
  }
}

// ...

// Get the model's response.
var text = response.Text;

// Get the grounding metadata.
var groundingMetadata = response.Candidates.First().GroundingMetadata.Value;

// REQUIRED - display Google Search suggestions
// (renderedContent contains HTML and CSS for the search widget)
if (groundingMetadata.SearchEntryPoint.HasValue) {
    var renderedContent = groundingMetadata.SearchEntryPoint.Value.RenderedContent;
    // TODO(developer): Display Google Search suggestions using a WebView
}

// REQUIRED - display sources
foreach(GroundingChunk chunk in groundingMetadata.GroundingChunks) {
    var title = chunk.Web.Value.Title;  // for example, "uefa.com"
    var uri = chunk.Web.Value.Uri;  // for example, "https://vertexaisearch.cloud.google.com..."
    // TODO(developer): show sources in the UI
}