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Grounding with Google Search connects the Gemini model to real-time web content
and works with all available languages. This allows
Gemini to provide more accurate answers and cite verifiable sources beyond its
knowledge cutoff.
Grounding helps you build applications that can:
Increase factual accuracy: Reduce model hallucinations by basing responses on real-world information.
Access real-time information: Answer questions about recent events and topics.
Provide citations: Build user trust by showing the sources for the
model's claims.
Python
from google import genai
from google.genai import types
client = genai.Client()
grounding_tool = types.Tool(
google_search=types.GoogleSearch()
)
config = types.GenerateContentConfig(
tools=[grounding_tool]
)
response = client.models.generate_content(
model="gemini-3-flash-preview",
contents="Who won the euro 2024?",
config=config,
)
print(response.text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
const groundingTool = {
googleSearch: {},
};
const config = {
tools: [groundingTool],
};
const response = await ai.models.generateContent({
model: "gemini-3-flash-preview",
contents: "Who won the euro 2024?",
config,
});
console.log(response.text);
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-X POST \
-d '{
"contents": [
{
"parts": [
{"text": "Who won the euro 2024?"}
]
}
],
"tools": [
{
"google_search": {}
}
]
}'
You can learn more by trying the Search tool
notebook.
How grounding with Google Search works
When you enable the google_search tool, the model handles the entire workflow
of searching, processing, and citing information automatically.
[image: google-search-tool-overview.png]
User Prompt: Your application sends a user's prompt to the Gemini API with the google_search tool enabled.
Prompt Analysis: The model analyzes the prompt and determines if a Google Search can improve the answer.
Google Search: If needed, the model automatically generates one or multiple search queries and executes them.
Search Results Processing: The model processes the search results, synthesizes the information, and formulates a response.
Grounded Response: The API returns a final, user-friendly response that is grounded in the search results. This response includes the model's text answer and groundingMetadata with the search queries, web results, and citations.
Understanding the grounding response
When a response is successfully grounded, the response includes a
groundingMetadata field. This structured data is essential for verifying
claims and building a rich citation experience in your application.
{
"candidates": [
{
"content": {
"parts": [
{
"text": "Spain won Euro 2024, defeating England 2-1 in the final. This victory marks Spain's record fourth European Championship title."
}
],
"role": "model"
},
"groundingMetadata": {
"webSearchQueries": [
"UEFA Euro 2024 winner",
"who won euro 2024"
],
"searchEntryPoint": {
"renderedContent": "<!-- HTML and CSS for the search widget -->"
},
"groundingChunks": [
{"web": {"uri": "https://vertexaisearch.cloud.google.com.....", "title": "aljazeera.com"}},
{"web": {"uri": "https://vertexaisearch.cloud.google.com.....", "title": "uefa.com"}}
],
"groundingSupports": [
{
"segment": {"startIndex": 0, "endIndex": 85, "text": "Spain won Euro 2024, defeatin..."},
"groundingChunkIndices": [0]
},
{
"segment": {"startIndex": 86, "endIndex": 210, "text": "This victory marks Spain's..."},
"groundingChunkIndices": [0, 1]
}
]
}
}
]
}
The Gemini API returns the following information with the groundingMetadata:
webSearchQueries : Array of the search queries used. This is useful for debugging and understanding the model's reasoning process.
searchEntryPoint : Contains the HTML and CSS to render the required Search Suggestions. Full usage requirements are detailed in the Terms of
Service.
groundingChunks : Array of objects containing the web sources (uri and title).
groundingSupports : Array of chunks to connect model response text to the sources in groundingChunks. Each chunk links a text segment (defined by startIndex and endIndex) to one or more groundingChunkIndices. This is the key to building inline citations.
Grounding with Google Search can also be used in combination with the URL
context tool to ground responses in both public
web data and the specific URLs you provide.
Attributing sources with inline citations
The API returns structured citation data, giving you complete control over how
you display sources in your user interface. You can use the groundingSupports
and groundingChunks fields to link the model's statements directly to their
sources. Here is a common pattern for processing the metadata to create a
response with inline, clickable citations.
Python
def add_citations(response):
text = response.text
supports = response.candidates[0].grounding_metadata.grounding_supports
chunks = response.candidates[0].grounding_metadata.grounding_chunks
# Sort supports by end_index in descending order to avoid shifting issues when inserting.
sorted_supports = sorted(supports, key=lambda s: s.segment.end_index, reverse=True)
for support in sorted_supports:
end_index = support.segment.end_index
if support.grounding_chunk_indices:
# Create citation string like [1](link1)[2](link2)
citation_links = []
for i in support.grounding_chunk_indices:
if i < len(chunks):
uri = chunks[i].web.uri
citation_links.append(f"[{i + 1}]({uri})")
citation_string = ", ".join(citation_links)
text = text[:end_index] + citation_string + text[end_index:]
return text
# Assuming response with grounding metadata
text_with_citations = add_citations(response)
print(text_with_citations)
JavaScript
function addCitations(response) {
let text = response.text;
const supports = response.candidates[0]?.groundingMetadata?.groundingSupports;
const chunks = response.candidates[0]?.groundingMetadata?.groundingChunks;
// Sort supports by end_index in descending order to avoid shifting issues when inserting.
const sortedSupports = [...supports].sort(
(a, b) => (b.segment?.endIndex ?? 0) - (a.segment?.endIndex ?? 0),
);
for (const support of sortedSupports) {
const endIndex = support.segment?.endIndex;
if (endIndex === undefined || !support.groundingChunkIndices?.length) {
continue;
}
const citationLinks = support.groundingChunkIndices
.map(i => {
const uri = chunks[i]?.web?.uri;
if (uri) {
return `[${i + 1}](${uri})`;
}
return null;
})
.filter(Boolean);
if (citationLinks.length > 0) {
const citationString = citationLinks.join(", ");
text = text.slice(0, endIndex) + citationString + text.slice(endIndex);
}
}
return text;
}
const textWithCitations = addCitations(response);
console.log(textWithCitations);
The new response with inline citations will look like this:
Spain won Euro 2024, defeating England 2-1 in the final.[1](https:/...), [2](https:/...), [4](https:/...), [5](https:/...) This victory marks Spain's record-breaking fourth European Championship title.[5]((https:/...), [2](https:/...), [3](https:/...), [4](https:/...)
Pricing
When you use Grounding with Google Search with Gemini 3, your project is billed
for each search query that the model decides to execute. If the model decides to
execute multiple search queries to answer a single prompt (for example,
searching for "UEFA Euro 2024 winner" and "Spain vs England Euro 2024 final score" within the same API call), this counts as two billable uses of the tool
for that request. For billing purposes, we ignore the empty web search queries when counting unique queries. This billing model only applies to Gemini 3 models; when you use search
grounding with Gemini 2.5 or older models, your project is billed per prompt.
For detailed pricing information, see the Gemini API pricing
page.
Supported models
Experimental and Preview models are not included. You can find their
capabilities on the model
overview page.
Model
Grounding with Google Search
Gemini 2.5 Pro
️
Gemini 2.5 Flash
️
Gemini 2.5 Flash-Lite
️
Gemini 2.0 Flash
️
| Note: Older models use a google_search_retrieval tool. For all current models, use the google_search tool as shown in the examples.
Supported tools combinations
You can use Grounding with Google Search with other tools like
code execution and
URL context to power more complex use cases.
What's next
Try the Grounding with Google Search in the Gemini API
Cookbook.
Learn about other available tools, like Function Calling.
Learn how to augment prompts with specific URLs using the URL context
tool.
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 { const baseUrl = "https://www.google.co.il/search?q=site:mitmachim.top"; const m = location.pathname.match(/^\/(topic|category)\/(\d+)/); const type = m ? m[1] : null; const id = m ? m[2] : null; let searchInput; function search(path = "") { const query = searchInput.value.trim(); if (query) { window.open(`${baseUrl}${path}%20${encodeURIComponent(query)}`, "_blank"); } } let buttons = {}; buttons.searchAll = { id: "bb-search-all", label: `חיפוש ${type ? "בכל הפורום" : ""}`, className: type ? "btn-secondary" : "btn-primary", callback: () => search(), }; if (type === "topic") { buttons.searchTopic = { label: "חיפוש בנושא זה", className: "btn-primary", callback: () => search(`/topic/${id}`), }; } else if (type === "category") { buttons.searchCategory = { label: "חיפוש בקטגוריה זו", className: "btn-primary", callback: () => search(`/category/${id}`), }; } buttons.cancel = { label: "ביטול", className: "btn-default", "data-bs-dismiss": "modal" }; const dialog = bootbox.dialog({ title: '<div style="text-align:center;"><img src="/assets/uploads/files/1625950269825-mt-google.png" width="400" alt="חיפוש בגוגל"></div>', message: '<input id="bb-inp" class="bootbox-input bootbox-input-text form-control" placeholder="הקלידו כדי לחפש בגוגל" autocomplete="off"/>', buttons, backdrop: true, show: true, onEscape: true }); dialog.on("shown.bs.modal", function () { searchInput = document.getElementById("bb-inp"); if (searchInput) { searchInput.focus(); searchInput.addEventListener("keydown", function (e) { if (e.key === "Enter") { e.preventDefault(); const query = this.value.trim(); if (query) { search(); dialog.modal("hide"); } } else if (e.key === "Escape") { e.preventDefault(); dialog.modal("hide"); } }); } const searchAllButton = document.getElementById("bb-search-all"); if (searchAllButton) { searchAllButton.title = "לחצו על אנטר כדי לחפש"; } }); })(); void 0;){kind=link}