"""
Example: Analyze files directly from Google Cloud Storage (GCS).
The Gemini API now supports GCS URIs natively (up to 2GB).
No need to download or re-upload - just pass the gs:// URI directly.
Requirements:
- Vertex AI must be enabled (GCS URIs require OAuth, not API keys)
- Run: gcloud auth application-default login
- Set environment variables: GOOGLE_CLOUD_PROJECT, GOOGLE_CLOUD_LOCATION
- Your GCS bucket must be accessible to your credentials
Supported formats: PDF, JSON, HTML, CSS, XML, images (PNG, JPEG, WebP, GIF)
"""
from agno.agent import Agent
from agno.media import File
from agno.models.google import Gemini
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
# GCS requires Vertex AI (OAuth credentials), not API keys
# Set GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_LOCATION env vars
agent = Agent(
model=Gemini(
id="gemini-3-flash-preview",
vertexai=True,
),
markdown=True,
)
# Pass GCS URI directly - no download or re-upload needed
agent.print_response(
"Summarize this document and extract key insights.",
files=[
File(
url="gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf", # Sample PDF
mime_type="application/pdf",
)
],
)
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
pass
Run the Example
# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/90_models/google/gemini
# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate
python gcs_file_input.py