> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Vertex AI Search with Gemini

> Vertex AI Search allows Gemini to search through your data stores, providing grounded responses based on your private knowledge base.

```python vertex_ai_search.py theme={null}
"""Vertex AI Search with Gemini.

Vertex AI Search allows Gemini to search through your data stores,
providing grounded responses based on your private knowledge base.

Prerequisites:
1. Set up Vertex AI Search datastore in Google Cloud Console
2. Export environment variables:
   export GOOGLE_GENAI_USE_VERTEXAI="true"
   export GOOGLE_CLOUD_PROJECT="your-project-id"
   export GOOGLE_CLOUD_LOCATION="your-location"

Run `uv pip install google-generativeai` to install dependencies.
"""

from agno.agent import Agent
from agno.models.google import Gemini

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

# Replace with your actual Vertex AI Search datastore ID
# Format: "projects/{project_id}/locations/{location}/collections/default_collection/dataStores/{datastore_id}"
datastore_id = "projects/your-project-id/locations/global/collections/default_collection/dataStores/your-datastore-id"

agent = Agent(
    model=Gemini(
        id="gemini-2.5-flash",
        vertexai_search=True,
        vertexai_search_datastore=datastore_id,
        vertexai=True,  # Use Vertex AI endpoint
    ),
    markdown=True,
)

# Ask questions that can be answered from your knowledge base
agent.print_response("What are our company's policies regarding remote work?")

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno google-genai
    ```
  </Step>

  <Step title="Export environment variables">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export GOOGLE_CLOUD_LOCATION="your_google_cloud_location_here"
      export GOOGLE_CLOUD_PROJECT="your_google_cloud_project_here"
      ```

      ```bash Windows theme={null}
      $Env:GOOGLE_CLOUD_LOCATION="your_google_cloud_location_here"
      $Env:GOOGLE_CLOUD_PROJECT="your_google_cloud_project_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Authenticate with Google Cloud">
    Sign in with Application Default Credentials:

    ```bash theme={null}
    gcloud auth application-default login
    ```
  </Step>

  <Step title="Run the example">
    Save the code above as `vertex_ai_search.py`, then run:

    ```bash theme={null}
    python vertex_ai_search.py
    ```
  </Step>
</Steps>

Full source: [cookbook/90\_models/google/gemini/vertex\_ai\_search.py](https://github.com/agno-agi/agno/blob/main/cookbook/90_models/google/gemini/vertex_ai_search.py)