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Grounding

Grounding enables Gemini to generate responses anchored in specific, verifiable information from external data sources. This reduces hallucinations and provides up-to-date, factual responses with citations.

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Why Ground Your Responses?

Reduce Hallucinations

Anchor responses in verified data sources

Real-time Information

Access current data beyond training cutoff

Verifiable Citations

Provide sources for transparency and trust

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Grounding Sources

Vertex AI supports multiple grounding sources:
  1. Google Search: Public web results with citations
  2. Enterprise Web Search: Compliant web search without logging
  3. Vertex AI Search: Your custom data stores
  4. Google Maps: Location and business data

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Google Search Grounding

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Basic Example

Ground responses in Google Search results: 
from google import genai
from google.genai.types import Tool, GoogleSearch, GenerateContentConfig

client = genai.Client(vertexai=True, project=PROJECT_ID, location=LOCATION)

google_search_tool = Tool(google_search=GoogleSearch())

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="Who won the 2024 UEFA European Championship?",
    config=GenerateContentConfig(
        tools=[google_search_tool]
    )
)

print(response.text)

​
View Grounding Metadata

Access search queries and citations: 
candidate = response.candidates[0]
metadata = candidate.grounding_metadata

# View search queries used
print(f"Search queries: {metadata.web_search_queries}")

# View grounding sources
for chunk in metadata.grounding_chunks:
    if chunk.web:
        print(f"Title: {chunk.web.title}")
        print(f"URL: {chunk.web.uri}")

​
Helper Function for Citations

Display responses with inline citations: 
from google.genai.types import GenerateContentResponse
from IPython.display import Markdown, display

def print_grounding_data(response: GenerateContentResponse) -> None:
    """Print response with grounding citations in Markdown."""
    candidate = response.candidates[0] if response.candidates else None
    metadata = getattr(candidate, "grounding_metadata", None)
    
    if not metadata:
        display(Markdown(response.text))
        return
    
    ENCODING = "utf-8"
    text_bytes = response.text.encode(ENCODING)
    parts = []
    last = 0
    
    # Insert citation markers
    for support in metadata.grounding_supports or []:
        end = support.segment.end_index
        parts.append(text_bytes[last:end].decode(ENCODING))
        parts.append(" " + "".join(f"[{i + 1}]" for i in support.grounding_chunk_indices))
        last = end
    
    parts.append(text_bytes[last:].decode(ENCODING))
    parts.append("\n\n----\n## Sources\n")
    
    # List sources
    if chunks := metadata.grounding_chunks:
        for i, chunk in enumerate(chunks, 1):
            if ctx := chunk.web:
                uri = ctx.uri.replace(" ", "%20")
                parts.append(f"{i}. [{ctx.title}]({uri})\n")
    
    display(Markdown("".join(parts)))

# Use the helper
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="What happened at the 2024 Olympics?",
    config=GenerateContentConfig(tools=[google_search_tool])
)

print_grounding_data(response)

​
Multimodal Grounding

Ground multimodal queries: 
from google.genai.types import Part

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents=[
        Part.from_uri(
            file_uri="gs://path/to/paris.jpg",
            mime_type="image/jpeg"
        ),
        "What is the current temperature at this location?"
    ],
    config=GenerateContentConfig(
        tools=[google_search_tool]
    )
)

print_grounding_data(response)
For compliance-sensitive applications, use Enterprise Web Search: 
from google.genai.types import Tool, EnterpriseWebSearch

enterprise_search_tool = Tool(
    enterprise_web_search=EnterpriseWebSearch()
)

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="Latest AI research findings",
    config=GenerateContentConfig(
        tools=[enterprise_search_tool]
    )
)

print_grounding_data(response)
Key Differences:
  • No customer query logging
  • VPC Service Controls support
  • Multi-region processing (US/EU)
  • Same citation format as Google Search

​
Google Maps Grounding

Ground responses in Google Maps location data: 
from google.genai.types import (
    Tool,
    GoogleMaps,
    ToolConfig,
    RetrievalConfig,
    LatLng
)

google_maps_tool = Tool(google_maps=GoogleMaps())

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="Recommend some good vegetarian restaurants in Las Vegas.",
    config=GenerateContentConfig(
        system_instruction="You are a helpful assistant with access to map data.",
        tools=[google_maps_tool],
        tool_config=ToolConfig(
            retrieval_config=RetrievalConfig(
                lat_lng=LatLng(
                    latitude=36.1699,
                    longitude=-115.1398
                )
            )
        )
    )
)

print_grounding_data(response)

​
Maps Grounding Metadata

Access place IDs and location details: 
for chunk in response.candidates[0].grounding_metadata.grounding_chunks:
    if chunk.maps:
        print(f"Place: {chunk.maps.title}")
        print(f"Place ID: {chunk.maps.place_id}")
        print(f"Address: {chunk.maps.text}")

​
Vertex AI Search Grounding

​
Create a Data Store

First, create a Vertex AI Search data store with your custom data:
1

Enable APIs

Enable the Vertex AI Search API
2

Create Data Store

Create a data store with unstructured data from Cloud Storage:
  • Go to Vertex AI Search console
  • Click “Create Data Store”
  • Choose “Unstructured documents”
  • Point to your GCS bucket: gs://your-bucket/documents/
3

Create Search App

Create a search app and enable Enterprise edition features
4

Wait for Indexing

Wait for data ingestion to complete (may take several minutes)

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Use Your Data Store

from google.genai.types import Tool, Retrieval, VertexAISearch

# Configure data store
VERTEX_AI_SEARCH_APP_ID = "your-app-id"
VERTEX_AI_SEARCH_ENGINE = (
    f"projects/{PROJECT_ID}/locations/global/"
    f"collections/default_collection/engines/{VERTEX_AI_SEARCH_APP_ID}"
)

vertex_ai_search_tool = Tool(
    retrieval=Retrieval(
        vertex_ai_search=VertexAISearch(
            engine=VERTEX_AI_SEARCH_ENGINE
        )
    )
)

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="What is the company culture like?",
    config=GenerateContentConfig(
        tools=[vertex_ai_search_tool]
    )
)

print_grounding_data(response)

​
Private Data Example

Query internal documents: 
# Example: Internal HR documents
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="How do I book business travel?",
    config=GenerateContentConfig(
        tools=[vertex_ai_search_tool]
    )
)

print(response.text)

# View source documents
for chunk in response.candidates[0].grounding_metadata.grounding_chunks:
    if chunk.retrieved_context:
        print(f"Document: {chunk.retrieved_context.title}")
        print(f"URI: {chunk.retrieved_context.uri}")

​
Grounding in Chat

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Google Search Chat

Maintain grounded conversations: 
chat = client.chats.create(
    model="gemini-3-flash-preview",
    config=GenerateContentConfig(
        tools=[Tool(google_search=GoogleSearch())]
    )
)

# First question
response = chat.send_message("What are managed datasets in Vertex AI?")
print_grounding_data(response)

# Follow-up with context
response = chat.send_message("What types of data can I use?")
print_grounding_data(response)

​
Vertex AI Search Chat

Chat with custom data: 
chat = client.chats.create(
    model="gemini-3-flash-preview",
    config=GenerateContentConfig(
        tools=[vertex_ai_search_tool]
    )
)

response = chat.send_message("How do I request time off?")
print_grounding_data(response)

response = chat.send_message("What's the approval process?")
print_grounding_data(response)

​
Combined Grounding

Use multiple grounding sources: 
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="Compare public AI research with our internal guidelines",
    config=GenerateContentConfig(
        tools=[
            Tool(google_search=GoogleSearch()),
            vertex_ai_search_tool
        ]
    )
)

​
Search Entry Points

For production applications, add a Search Entry Point: 
metadata = response.candidates[0].grounding_metadata

if metadata.search_entry_point:
    print("Search Entry Point:")
    print(metadata.search_entry_point.rendered_content)
When using Google Search grounding in production, you must display the Search Entry Point to comply with Google Search terms of service. See documentation.

​
Grounding Configuration

Fine-tune grounding behavior: 
from google.genai.types import (
    ToolConfig,
    FunctionCallingConfig,
    FunctionCallingConfigMode
)

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="Your query",
    config=GenerateContentConfig(
        tools=[google_search_tool],
        tool_config=ToolConfig(
            function_calling_config=FunctionCallingConfig(
                mode=FunctionCallingConfigMode.ANY  # Force grounding
            )
        )
    )
)

​
Response Filtering

Check grounding confidence: 
candidate = response.candidates[0]
metadata = candidate.grounding_metadata

# Check if response is grounded
if metadata and metadata.grounding_chunks:
    print(f"Found {len(metadata.grounding_chunks)} sources")
    
    # Check support for each statement
    for support in metadata.grounding_supports:
        confidence = support.confidence_scores[0] if support.confidence_scores else 0
        if confidence > 0.8:
            print(f"High confidence: {support.segment.text[:100]}...")
else:
    print("Warning: Response not grounded in sources")

​
Comparison: Ungrounded vs Grounded

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="What is today's date?"
)
print(response.text)
Result: “I’m a large language model, I don’t have access to real-time information.”

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Best Practices

Clear Queries

Write specific, focused queries for better grounding

Verify Sources

Always check grounding metadata for source quality

Handle Missing Data

Gracefully handle cases with no grounding results

Update Data Stores

Keep Vertex AI Search data stores current

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Error Handling

try:
    response = client.models.generate_content(
        model="gemini-3-flash-preview",
        contents="Your query",
        config=GenerateContentConfig(
            tools=[google_search_tool]
        )
    )
    
    if not response.candidates[0].grounding_metadata:
        print("Warning: No grounding data available")
    else:
        print_grounding_data(response)
        
except Exception as e:
    print(f"Grounding error: {e}")

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Next Steps

Function Calling

Combine grounding with function calls

Context Caching

Cache grounded data for efficiency

Multimodal

Ground multimodal queries

Batch Prediction

Process grounded queries at scale

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Resources

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