@ai.tool() Decorator
Defines a tool.@ai.tool()
async def get_weather(city: str) -> dict:
"""Gets the current weather for a city.
Args:
city: The name of the city
Returns:
Weather information including temperature and conditions
"""
# Tool implementation
return {
"temperature": 72,
"conditions": "sunny",
"humidity": 45,
}
Parameters
str | None
Tool name (defaults to function name)
str | None
Tool description (defaults to function docstring)
Using Tools
# Use by name
response = await ai.generate(
prompt="What's the weather in Paris?",
tools=["get_weather"],
)
Tool Context
from genkit import ToolRunContext
@ai.tool()
async def my_tool(city: str, context: ToolRunContext) -> dict:
"""Tool with access to context."""
# Access context information
print(f"Trace ID: {context.trace_id}")
return {"result": "data"}
Tool Response
from genkit import tool_response
# Create a tool response part
response_part = tool_response(
name="get_weather",
output={"temperature": 72},
)
Example: Complete Tool
import asyncio
from genkit import Genkit
from pydantic import BaseModel
ai = Genkit()
class WeatherInput(BaseModel):
"""Weather query input."""
city: str
units: str = "fahrenheit"
class WeatherOutput(BaseModel):
"""Weather information."""
temperature: int
conditions: str
city: str
@ai.tool()
async def get_weather(input: WeatherInput) -> WeatherOutput:
"""Gets current weather for a city.
Args:
input: Weather query with city and units
Returns:
Current weather information
"""
# In real implementation, call weather API
return WeatherOutput(
temperature=72,
conditions="sunny",
city=input.city,
)
@ai.flow()
async def weather_assistant(query: str) -> str:
"""Weather assistant with tool access."""
response = await ai.generate(
prompt=query,
tools=["get_weather"],
)
return response.text
async def main():
result = await weather_assistant(
"What's the weather like in San Francisco?"
)
print(result)
if __name__ == "__main__":
asyncio.run(main())