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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""Hello Google GenAI Vertex AI sample."""
import argparse
import base64
import pathlib
from enum import Enum
import structlog
from genkit.ai import Genkit, Media, MediaPart, TextPart
from genkit.plugins.google_genai import GeminiImageConfigSchema, VertexAI
from genkit.types import GenerationCommonConfig
logger = structlog.get_logger(__name__)
ai = Genkit(
plugins=[
VertexAI(location='us-central1'),
],
model='vertexai/gemini-2.5-flash',
)
class ThinkingLevel(str, Enum):
LOW = 'LOW'
HIGH = 'HIGH'
@ai.flow()
async def thinking_level_pro(level: ThinkingLevel):
"""Gemini 3.0 thinkingLevel config (Pro)."""
response = await ai.generate(
model='vertexai/gemini-3-pro-preview',
prompt=(
'Alice, Bob, and Carol each live in a different house on the '
'same street: red, green, and blue. The person who lives in the red house '
'owns a cat. Bob does not live in the green house. Carol owns a dog. The '
'green house is to the left of the red house. Alice does not own a cat. '
'The person in the blue house owns a fish. '
'Who lives in each house, and what pet do they own? Provide your '
'step-by-step reasoning.'
),
config={
'thinking_config': {
'include_thoughts': True,
'thinking_level': level.value,
}
},
)
return response.text
class ThinkingLevelFlash(str, Enum):
MINIMAL = 'MINIMAL'
LOW = 'LOW'
MEDIUM = 'MEDIUM'
HIGH = 'HIGH'
@ai.flow()
async def thinking_level_flash(level: ThinkingLevelFlash):
"""Gemini 3.0 thinkingLevel config (Flash)."""
response = await ai.generate(
model='vertexai/gemini-3-flash-preview',
prompt=(
'Alice, Bob, and Carol each live in a different house on the '
'same street: red, green, and blue. The person who lives in the red house '
'owns a cat. Bob does not live in the green house. Carol owns a dog. The '
'green house is to the left of the red house. Alice does not own a cat. '
'The person in the blue house owns a fish. '
'Who lives in each house, and what pet do they own? Provide your '
'step-by-step reasoning.'
),
config={
'thinking_config': {
'include_thoughts': True,
'thinking_level': level.value,
}
},
)
return response.text
@ai.flow()
async def video_understanding_metadata():
"""Video understanding with metadata."""
response = await ai.generate(
model='vertexai/gemini-2.5-flash',
prompt=[
MediaPart(
media=Media(url='gs://cloud-samples-data/video/animals.mp4', content_type='video/mp4'),
metadata={
'videoMetadata': {
'fps': 0.5,
'startOffset': '3.5s',
'endOffset': '10.2s',
}
},
),
TextPart(text='describe this video'),
],
)
return response.text
@ai.flow()
async def maps_grounding():
"""Google maps grounding."""
response = await ai.generate(
model='vertexai/gemini-2.5-flash',
prompt='Describe some sights near me',
config={
'tools': [{'googleMaps': {'enableWidget': True}}],
'retrieval_config': {
'latLng': {
'latitude': 43.0896,
'longitude': -79.0849,
},
},
},
)
return response.text
@ai.flow()
async def search_grounding():
"""Search grounding."""
response = await ai.generate(
model='vertexai/gemini-2.5-flash',
prompt='Who is Albert Einstein?',
config={'tools': [{'googleSearch': {}}]},
)
return response.text
@ai.flow()
async def gemini_media_resolution():
"""Media resolution."""
# Placeholder base64 for sample
plant_b64 = 'iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+A8AAQUBAScY42YAAAAASUVORK5CYII='
response = await ai.generate(
model='vertexai/gemini-3-pro-preview',
prompt=[
TextPart(text='What is in this picture?'),
MediaPart(
media=Media(url=f'data:image/png;base64,{plant_b64}'),
metadata={'mediaResolution': {'level': 'MEDIA_RESOLUTION_HIGH'}},
),
],
)
return response.text
@ai.flow()
async def gemini_image_editing():
"""Image editing with Gemini."""
plant_path = pathlib.Path(__file__).parent.parent / 'palm_tree.png'
room_path = pathlib.Path(__file__).parent.parent / 'my_room.png'
with open(plant_path, 'rb') as f:
plant_b64 = base64.b64encode(f.read()).decode('utf-8')
with open(room_path, 'rb') as f:
room_b64 = base64.b64encode(f.read()).decode('utf-8')
response = await ai.generate(
model='vertexai/gemini-2.5-flash-image-preview',
prompt=[
TextPart(text='add the plant to my room'),
MediaPart(media=Media(url=f'data:image/png;base64,{plant_b64}')),
MediaPart(media=Media(url=f'data:image/png;base64,{room_b64}')),
],
config=GeminiImageConfigSchema(
response_modalities=['TEXT', 'IMAGE'],
image_config={'aspect_ratio': '1:1'},
).model_dump(exclude_none=True),
)
for part in response.message.content:
if isinstance(part.root, MediaPart):
return part.root.media
return None
@ai.flow()
async def nano_banana_pro():
"""Nano banana pro config."""
response = await ai.generate(
model='vertexai/gemini-3-pro-image-preview',
prompt='Generate a picture of a sunset in the mountains by a lake',
config={
'response_modalities': ['TEXT', 'IMAGE'],
'image_config': {
'aspect_ratio': '3:4',
'image_size': '1K',
},
},
)
return response.media
@ai.flow()
async def imagen_image_generation():
"""A simple example of image generation with Gemini (Imagen)."""
response = await ai.generate(
model='vertexai/imagen-3.0-generate-002',
prompt='generate an image of a banana riding a bicycle',
)
return response.media
@ai.tool(name='getWeather')
def get_weather(location: str) -> dict:
"""Used to get current weather for a location."""
return {
'location': location,
'temperature_celcius': 21.5,
'conditions': 'cloudy',
}
@ai.tool(name='celsiusToFahrenheit')
def celsius_to_fahrenheit(celsius: float) -> float:
"""Converts Celsius to Fahrenheit."""
return (celsius * 9) / 5 + 32
@ai.flow()
async def tool_calling(location: str = 'Paris, France'):
"""Tool calling with Gemini."""
response = await ai.generate(
model='vertexai/gemini-2.5-flash',
tools=['getWeather', 'celsiusToFahrenheit'],
prompt=f"What's the weather in {location}? Convert the temperature to Fahrenheit.",
config=GenerationCommonConfig(temperature=1),
)
return response.text
async def main() -> None:
"""Main function."""
# Example run logic can go here or be empty for pure flow server
pass
if __name__ == '__main__':
ai.run_main(main())
