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agent-framework/python/samples/02-agents/tools/function_tool_with_kwargs.py
T
Copilot b05fc9e849 Python: Add load_dotenv() to samples for .env file support (#4043)
* Initial plan

* Add load_dotenv() to 303 Python samples for environment variable loading

Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com>

* Update SAMPLE_GUIDELINES.md to document load_dotenv() requirement

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* Update samples README.md to document .env file usage

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* Run ruff format on all changed sample files

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* Clarify load_dotenv() usage in README - local dev vs production

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* Remove deprecated getting_started folder as requested

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* Document env_file_path parameter for per-client configuration

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* Merge main branch to resolve conflicts

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* Fix run_evaluation.py file that was empty in merge commit

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* Remove dotnet changes from merge - out of scope for this PR

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* Remove package and test changes from merge - only sample changes needed

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* Remove test_func_utils.py - only sample changes needed

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* Revert sample files not in original changeset - keep only load_dotenv additions

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* Move load_dotenv() outside snippet tag in 06_host_your_agent.py

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* Fix comment placement - move load_dotenv before code comments

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* Fix load_dotenv() placement across all samples - after docstring, before code comments

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* Merge latest main branch with load_dotenv changes

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* Remove non-sample changes from merge - keep only load_dotenv additions

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* Revert non-load_dotenv sample changes from merge

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* Fix run_evaluation.py - use main's improved version (file already had load_dotenv)

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* Manual update

* Manual update 2

* Fix Role usage and load_dotenv placement per PR review feedback

Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com>

* Fix Role usage - use string literals not enum attributes

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* Fix SAMPLE_GUIDELINES.md example - load_dotenv before docstring per guidance

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* Move load_dotenv() before docstrings in all samples per SAMPLE_GUIDELINES ordering

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* Address PR review: rename files, fix placement, add session usage, remove note

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* Update Redis README to reference renamed file redis_history_provider.py

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---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com>
Co-authored-by: Tao Chen <taochen@microsoft.com>
Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2026-02-19 10:55:13 +00:00

61 lines
2.0 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
from typing import Annotated, Any
from agent_framework import tool
from agent_framework.openai import OpenAIResponsesClient
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 custom keyword arguments (kwargs) into an AI function
from the agent's run method, without exposing them 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.
"""
# Define the function tool with **kwargs to accept injected arguments
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
# see samples/02-agents/tools/function_tool_with_approval.py
# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
@tool(approval_mode="never_require")
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
**kwargs: Any,
) -> str:
"""Get the weather for a given location."""
# Extract the injected argument from kwargs
user_id = 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 = OpenAIResponsesClient().as_agent(
name="WeatherAgent",
instructions="You are a helpful weather assistant.",
tools=[get_weather],
)
# Pass the injected argument when running the agent
# The 'user_id' kwarg will be passed down to the tool execution via **kwargs
response = await agent.run("What is the weather like in Amsterdam?", user_id="user_123")
print(f"Agent: {response.text}")
if __name__ == "__main__":
asyncio.run(main())