db620d33df
CodeQL / Analyze (csharp) (push) Waiting to run
CodeQL / Analyze (python) (push) Waiting to run
dotnet-build-and-test / dotnet-test-functions (push) Has been cancelled
dotnet-build-and-test / paths-filter (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Debug, windows-latest, net9.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, ubuntu-latest, net10.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, ubuntu-latest, net8.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, windows-latest, net472) (push) Has been cancelled
dotnet-build-and-test / dotnet-test (Release, integration, true, ubuntu-latest, net10.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-test (Release, integration, true, windows-latest, net472) (push) Has been cancelled
dotnet-build-and-test / dotnet-foundry-hosted-it (push) Has been cancelled
dotnet-build-and-test / dotnet-build-and-test-check (push) Has been cancelled
dotnet-build-and-test / Integration Test Report (push) Has been cancelled
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
|
|
|
|
import asyncio
|
|
from typing import Annotated
|
|
|
|
from agent_framework import Agent, FunctionInvocationContext, tool
|
|
from agent_framework.openai import OpenAIChatClient
|
|
from dotenv import load_dotenv
|
|
from pydantic import Field
|
|
|
|
# Load environment variables from .env file
|
|
load_dotenv()
|
|
|
|
"""
|
|
AI Function with kwargs Example
|
|
|
|
This example demonstrates how to inject runtime context into an AI function
|
|
from the agent's run method, without exposing it to the AI model.
|
|
|
|
This is useful for passing runtime information like access tokens, user IDs, or
|
|
request-specific context that the tool needs but the model shouldn't know about
|
|
or provide. The injected context parameter can be typed as
|
|
``FunctionInvocationContext`` as shown here, or left untyped as ``ctx`` when you
|
|
prefer a lighter-weight sample setup.
|
|
"""
|
|
|
|
|
|
# Define the function tool with explicit invocation context.
|
|
# The context parameter can also be declared as an untyped ``ctx`` parameter.
|
|
@tool(approval_mode="never_require")
|
|
def get_weather(
|
|
location: Annotated[str, Field(description="The location to get the weather for.")],
|
|
ctx: FunctionInvocationContext,
|
|
) -> str:
|
|
"""Get the weather for a given location."""
|
|
# Extract the injected argument from the explicit context
|
|
user_id = ctx.kwargs.get("user_id", "unknown")
|
|
|
|
# Simulate using the user_id for logging or personalization
|
|
print(f"Getting weather for user: {user_id}")
|
|
|
|
return f"The weather in {location} is cloudy with a high of 15°C."
|
|
|
|
|
|
async def main() -> None:
|
|
agent = Agent(
|
|
client=OpenAIChatClient(),
|
|
name="WeatherAgent",
|
|
instructions="You are a helpful weather assistant.",
|
|
tools=[get_weather],
|
|
)
|
|
|
|
# Pass the runtime context explicitly when running the agent.
|
|
response = await agent.run(
|
|
"What is the weather like in Amsterdam?",
|
|
function_invocation_kwargs={"user_id": "user_123"},
|
|
)
|
|
|
|
print(f"Agent: {response.text}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|