Grounding

In generative AI, grounding is the ability to connect model output to verifiable sources of information. If you provide models with access to specific data sources, then grounding tethers their output to these data and reduces the chances of inventing content.

With Vertex AI, you can ground model outputs in the following ways:

  • Ground with Google Search - ground a model with publicly available web data.
  • Ground to your own data - ground a model with your own data from Vertex AI Search as a data store (Preview).

For more information about grounding, see Grounding overview.

Supported models

Model Version
Gemini 2.0 Flash-Lite with only text input gemini-2.0-flash-lite-001
Gemini 2.0 Flash with only text input gemini-2.0-flash-001

Parameter list

See examples for implementation details.

GoogleSearchRetrieval

Ground the response with public data.

Parameters

google_search_retrieval

Required: Object

Ground with publicly available web data.

Retrieval

Ground the response with private data from Vertex AI Search as a data store. Defines a retrieval tool that the model can call to access external knowledge.

Parameters

source

Required: VertexAISearch

Ground with Vertex AI Search data sources.

VertexAISearch

Parameters

datastore

Required: string

Fully-qualified data store resource ID from Vertex AI Search, in the following format: projects/{project}/locations/{location}/collections/default_collection/dataStores/{datastore}

Examples

Ground response on public web data using Google Search

Ground the response with Google Search public data. Include the google_search_retrieval tool in the request. No additional parameters are required.

Gen AI SDK for Python

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 ...'

Ground response on private data using Vertex AI Search

Ground the response with data from a Vertex AI Search data store. For more information, see Vertex AI Agent Builder.

Before you ground a response with private data, create a data store and a search app.

WARNING: For the time being, this "grounding" interface does not support Vertex AI Search "chunk mode".

Gen AI SDK for Python

from google import genai
from google.genai.types import (
    GenerateContentConfig,
    HttpOptions,
    Retrieval,
    Tool,
    VertexAISearch,
)

client = genai.Client(http_options=HttpOptions(api_version="v1"))

# Load Data Store ID from Vertex AI Search
# datastore = "projects/111111111111/locations/global/collections/default_collection/dataStores/data-store-id"

response = client.models.generate_content(
    model="gemini-2.0-flash-001",
    contents="How do I make an appointment to renew my driver's license?",
    config=GenerateContentConfig(
        tools=[
            # Use Vertex AI Search Tool
            Tool(
                retrieval=Retrieval(
                    vertex_ai_search=VertexAISearch(
                        datastore=datastore,
                    )
                )
            )
        ],
    ),
)

print(response.text)
# Example response:
# 'The process for making an appointment to renew your driver's license varies depending on your location. To provide you with the most accurate instructions...'

What's next

For detailed documentation, see the following: