# Copyright 2025 Google LLC
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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
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# SPDX-License-Identifier: Apache-2.0
"""Hello Vertex AI sample - Google Cloud's Vertex AI with Genkit.
This sample demonstrates how to use Vertex AI (Google Cloud's ML platform)
with Genkit for enterprise-grade AI applications.
Key Concepts (ELI5)::
┌─────────────────────┬────────────────────────────────────────────────────┐
│ Concept │ ELI5 Explanation │
├─────────────────────┼────────────────────────────────────────────────────┤
│ Vertex AI │ Google Cloud's AI platform. Like GoogleAI but │
│ │ for enterprise with more security features. │
├─────────────────────┼────────────────────────────────────────────────────┤
│ GCP Project │ Your Google Cloud project. Like a folder that │
│ │ holds all your cloud resources. │
├─────────────────────┼────────────────────────────────────────────────────┤
│ Location │ Which data center to use (us-central1, etc.). │
│ │ Pick one near your users. │
├─────────────────────┼────────────────────────────────────────────────────┤
│ ADC │ Application Default Credentials. Google's way │
│ │ of auto-finding your login credentials. │
├─────────────────────┼────────────────────────────────────────────────────┤
│ gcloud auth │ The command to log in. Run once and Google │
│ │ remembers who you are. │
└─────────────────────┴────────────────────────────────────────────────────┘
Key Features
============
| Feature Description | Example Function / Code Snippet |
|----------------------------------------------------------|----------------------------------------|
| Plugin Initialization | `ai = Genkit(plugins=[VertexAI(...)])` |
| Default Model Configuration | `ai = Genkit(model=...)` |
| Defining Flows | `@ai.flow()` decorator (multiple uses) |
| Defining Tools | `@ai.tool()` decorator (multiple uses) |
| Pydantic for Tool Input Schema | `GablorkenInput` |
| Simple Generation (Prompt String) | `say_hi` |
| Generation with Messages (`Message`, `Role`, `TextPart`) | `simple_generate_with_tools_flow` |
| Generation with Tools | `simple_generate_with_tools_flow` |
| Tool Response Handling | `simple_generate_with_interrupts` |
| Tool Interruption (`ctx.interrupt`) | `gablorken_tool2` |
| Embedding (`ai.embed`, `Document`) | `embed_docs` |
| Generation Configuration (`temperature`, etc.) | `say_hi_with_configured_temperature` |
| Streaming Generation (`ai.generate_stream`) | `say_hi_stream` |
| Streaming Chunk Handling (`ctx.send_chunk`) | `say_hi_stream`, `generate_character` |
| Structured Output (Schema) | `generate_character` |
| Pydantic for Structured Output Schema | `RpgCharacter` |
| Unconstrained Structured Output | `generate_character_unconstrained` |
Testing This Demo
=================
1. **Prerequisites**:
```bash
# Set GCP project and location
export GOOGLE_CLOUD_PROJECT=your_project_id
export GOOGLE_CLOUD_LOCATION=us-central1
# Authenticate with GCP
gcloud auth application-default login
```
2. **Run the demo**:
```bash
cd py/samples/google-genai-vertexai-hello
./run.sh
```
3. **Open DevUI** at http://localhost:4000
4. **Test basic flows**:
- [ ] `say_hi` - Simple generation
- [ ] `say_hi_stream` - Streaming response
- [ ] `say_hi_with_configured_temperature` - Custom config
5. **Test tools**:
- [ ] `simple_generate_with_tools_flow` - Tool calling
- [ ] `simple_generate_with_interrupts` - Tool interrupts
6. **Test structured output**:
- [ ] `generate_character` - Constrained output
- [ ] `generate_character_unconstrained` - Unconstrained
7. **Test embeddings**:
- [ ] `embed_docs` - Document embedding
8. **Note**: Vertex AI requires a GCP project with billing enabled.
"""
import os
from pydantic import BaseModel, Field
from rich.traceback import install as install_rich_traceback
from genkit.ai import Genkit, Output, ToolRunContext, tool_response
from genkit.blocks.model import GenerateResponseWrapper
from genkit.core.action import ActionRunContext
from genkit.core.logging import get_logger
from genkit.plugins.google_genai import (
EmbeddingTaskType,
VertexAI,
)
from genkit.types import (
Embedding,
GenerationCommonConfig,
Message,
Part,
Role,
TextPart,
)
install_rich_traceback(show_locals=True, width=120, extra_lines=3)
logger = get_logger(__name__)
# Check for GCLOUD_PROJECT or GOOGLE_CLOUD_PROJECT
# If GOOGLE_CLOUD_PROJECT is set but GCLOUD_PROJECT isn't, use it
if 'GCLOUD_PROJECT' not in os.environ:
if 'GOOGLE_CLOUD_PROJECT' in os.environ:
os.environ['GCLOUD_PROJECT'] = os.environ['GOOGLE_CLOUD_PROJECT']
else:
os.environ['GCLOUD_PROJECT'] = input('Please enter your GCLOUD_PROJECT_ID: ')
ai = Genkit(
plugins=[VertexAI()],
model='vertexai/gemini-3-pro-preview',
)
class CurrencyExchangeInput(BaseModel):
"""Currency exchange flow input schema."""
amount: float = Field(description='Amount to convert', default=100)
from_curr: str = Field(description='Source currency code', default='USD')
to_curr: str = Field(description='Target currency code', default='EUR')
class CurrencyInput(BaseModel):
"""Currency conversion input schema."""
amount: float = Field(description='Amount to convert', default=100)
from_currency: str = Field(description='Source currency code (e.g., USD)', default='USD')
to_currency: str = Field(description='Target currency code (e.g., EUR)', default='EUR')
class GablorkenInput(BaseModel):
"""The Pydantic model for tools."""
value: int = Field(description='value to calculate gablorken for')
class Skills(BaseModel):
"""Skills for an RPG character."""
strength: int = Field(description='strength (0-100)')
charisma: int = Field(description='charisma (0-100)')
endurance: int = Field(description='endurance (0-100)')
class RpgCharacter(BaseModel):
"""An RPG character."""
name: str = Field(description='name of the character')
back_story: str = Field(description='back story', alias='backStory')
abilities: list[str] = Field(description='list of abilities (3-4)')
skills: Skills
class SayHiInput(BaseModel):
"""Input for say_hi flow."""
name: str = Field(default='Mittens', description='Name to greet')
class StreamInput(BaseModel):
"""Input for streaming flow."""
name: str = Field(default='Shadow', description='Name for streaming greeting')
class CharacterInput(BaseModel):
"""Input for character generation."""
name: str = Field(default='Whiskers', description='Character name')
class TemperatureInput(BaseModel):
"""Input for temperature config flow."""
data: str = Field(default='Mittens', description='Name to greet')
class ToolsFlowInput(BaseModel):
"""Input for tools flow."""
value: int = Field(default=42, description='Value for gablorken calculation')
@ai.tool()
def convert_currency(input: CurrencyInput) -> str:
"""Convert currency amount.
Args:
input: Currency conversion parameters.
Returns:
Converted amount.
"""
# Mock conversion rates
rates = {
('USD', 'EUR'): 0.85,
('EUR', 'USD'): 1.18,
('USD', 'GBP'): 0.73,
('GBP', 'USD'): 1.37,
}
rate = rates.get((input.from_currency, input.to_currency), 1.0)
converted = input.amount * rate
return f'{input.amount} {input.from_currency} = {converted:.2f} {input.to_currency}'
@ai.flow()
async def currency_exchange(input: CurrencyExchangeInput) -> str:
"""Convert currency using tools.
Args:
input: Currency exchange parameters.
Returns:
Conversion result.
"""
response = await ai.generate(
prompt=f'Convert {input.amount} {input.from_curr} to {input.to_curr}',
tools=['convert_currency'],
)
return response.text
@ai.flow()
async def embed_docs(docs: list[str] | None = None) -> list[Embedding]:
"""Generate an embedding for the words in a list.
Args:
docs: list of texts (string)
Returns:
The generated embedding.
"""
if docs is None:
docs = ['Hello world', 'Genkit is great', 'Embeddings are fun']
options = {'task_type': EmbeddingTaskType.CLUSTERING}
return await ai.embed_many(
embedder='vertexai/text-embedding-004',
content=docs,
options=options,
)
@ai.tool(name='gablorkenTool')
def gablorken_tool(input_: GablorkenInput) -> int:
"""Calculate a gablorken.
Args:
input_: The input to calculate gablorken for.
Returns:
The calculated gablorken.
"""
return input_.value * 3 - 5
@ai.tool(name='gablorkenTool2')
def gablorken_tool2(input_: GablorkenInput, ctx: ToolRunContext) -> None:
"""The user-defined tool function.
Args:
input_: the input to the tool
ctx: the tool run context
Returns:
The calculated gablorken.
"""
ctx.interrupt()
@ai.flow()
async def generate_character(
input: CharacterInput,
ctx: ActionRunContext | None = None,
) -> RpgCharacter:
"""Generate an RPG character.
Args:
input: Input with character name.
ctx: the context of the tool
Returns:
The generated RPG character.
"""
if ctx is not None and ctx.is_streaming:
stream, result = ai.generate_stream(
prompt=f'generate an RPG character named {input.name}',
output=Output(schema=RpgCharacter),
)
async for data in stream:
ctx.send_chunk(data.output)
return (await result).output
else:
result = await ai.generate(
prompt=f'generate an RPG character named {input.name}',
output=Output(schema=RpgCharacter),
)
return result.output
@ai.flow()
async def generate_character_unconstrained(
input: CharacterInput,
ctx: ActionRunContext | None = None,
) -> RpgCharacter:
"""Generate an unconstrained RPG character.
Args:
input: Input with character name.
ctx: the context of the tool
Returns:
The generated RPG character.
"""
result = await ai.generate(
prompt=f'generate an RPG character named {input.name}',
output=Output(schema=RpgCharacter),
output_constrained=False,
output_instructions=True,
)
return result.output
@ai.flow()
async def say_hi(input: SayHiInput) -> str:
"""Generate a greeting for the given name.
Args:
input: Input with name to greet.
Returns:
The generated response with a function.
"""
resp = await ai.generate(
prompt=f'hi {input.name}',
)
return resp.text
@ai.flow()
async def say_hi_stream(
input: StreamInput,
ctx: ActionRunContext | None = None,
) -> str:
"""Generate a greeting for the given name.
Args:
input: Input with name for streaming.
ctx: the context of the tool
Returns:
The generated response with a function.
"""
stream, _ = ai.generate_stream(prompt=f'hi {input.name}')
result: str = ''
async for data in stream:
if ctx is not None:
ctx.send_chunk(data.text)
result += data.text
return result
@ai.flow()
async def say_hi_with_configured_temperature(input: TemperatureInput) -> GenerateResponseWrapper:
"""Generate a greeting for the given name.
Args:
input: Input with name to greet.
Returns:
The generated response with a function.
"""
return await ai.generate(
messages=[Message(role=Role.USER, content=[Part(root=TextPart(text=f'hi {input.data}'))])],
config=GenerationCommonConfig(temperature=0.1),
)
@ai.flow()
async def simple_generate_with_interrupts(input: ToolsFlowInput) -> str:
"""Generate a greeting for the given name.
Args:
input: Input with value for gablorken calculation.
Returns:
The generated response with a function.
"""
response1 = await ai.generate(
prompt=f'what is a gablorken of {input.value}',
tools=['gablorkenTool2'],
)
await logger.ainfo(f'len(response.tool_requests)={len(response1.tool_requests)}')
if len(response1.tool_requests) == 0:
return response1.text
tr = tool_response(response1.tool_requests[0], 178)
response = await ai.generate(
messages=response1.messages,
tool_responses=[tr],
tools=['gablorkenTool'],
)
return response.text
@ai.flow()
async def simple_generate_with_tools_flow(input: ToolsFlowInput) -> str:
"""Generate a greeting for the given name.
Args:
input: Input with value for gablorken calculation.
Returns:
The generated response with a function.
"""
response = await ai.generate(
prompt=f'what is a gablorken of {input.value}',
tools=['gablorkenTool'],
)
return response.text
async def main() -> None:
"""Main function - runs when script starts."""
await logger.ainfo('VertexAI Hello sample initialized. Use Dev UI to invoke flows.')
if __name__ == '__main__':
ai.run_main(main())
