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* Python: clean up kwargs across agents, chat clients, tools, and sessions (#3642) Audit and refactor public **kwargs usage across core agents, chat clients, tools, sessions, and provider packages per the migration strategy codified in CODING_STANDARD.md. Key changes: - Add explicit runtime buckets: function_invocation_kwargs and client_kwargs on RawAgent.run() and chat client get_response() layers. - Refactor FunctionTool to prefer explicit ctx: FunctionInvocationContext injection; legacy **kwargs tools still work via _forward_runtime_kwargs. - Refactor Agent.as_tool() to use direct JSON schema, always-streaming wrapper, approval_mode parameter, and UserInputRequiredException propagation (integrates PR #4568 behavior). - Remove implicit session bleeding into FunctionInvocationContext; tools that need a session must receive it via function_invocation_kwargs. - Lower chat-client layers after FunctionInvocationLayer accept only compatibility **kwargs (client_kwargs flattened, function_invocation_kwargs ignored). - Add layered docstring composition from Raw... implementations via _docstrings.py helper. - Clean up provider constructors to use explicit additional_properties. - Deprecation warnings on legacy direct kwargs paths. - Update samples, tests, and typing across all 23 packages. Resolves #3642 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * clarified docstring * feedback fixes * Add unit tests for _docstrings.py build/apply helpers Tests cover: no docstring source, no extra kwargs, appending to existing Keyword Args section, inserting after Args, inserting in plain docstrings, multiline descriptions, ordering, and apply_layered_docstring. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Add test for propagate_session TypeError on non-AgentSession values Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Add tests for multi-content and empty UserInputRequiredException propagation Cover the branching logic in _try_execute_function_calls for: - Multiple user_input_request items in a single exception (extra_user_input_contents path) - Empty contents list (fallback function_result path) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Add tests for DurableAIAgent.get_session forwarding service_session_id Verifies get_session correctly forwards service_session_id and session_id to the executor's get_new_session, replacing the removed kwargs test. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Simplify ag-ui test stub to read session from client_kwargs only Remove dual-mode detection (client_kwargs vs raw kwargs fallback) from the test mock. Session is now read exclusively from client_kwargs, matching the settled public calling convention. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated create and get sessions in durable * fixed docstrings * fix test * updated session handling * updated from main * updated tests --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from typing import Annotated
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from agent_framework import FunctionInvocationContext, tool
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from agent_framework.openai import OpenAIResponsesClient
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from dotenv import load_dotenv
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from pydantic import Field
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# Load environment variables from .env file
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load_dotenv()
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"""
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AI Function with kwargs Example
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This example demonstrates how to inject runtime context into an AI function
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from the agent's run method, without exposing it to the AI model.
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This is useful for passing runtime information like access tokens, user IDs, or
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request-specific context that the tool needs but the model shouldn't know about
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or provide. The injected context parameter can be typed as
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``FunctionInvocationContext`` as shown here, or left untyped as ``ctx`` when you
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prefer a lighter-weight sample setup.
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"""
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# Define the function tool with explicit invocation context.
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# The context parameter can also be declared as an untyped ``ctx`` parameter.
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@tool(approval_mode="never_require")
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def get_weather(
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location: Annotated[str, Field(description="The location to get the weather for.")],
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ctx: FunctionInvocationContext,
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) -> str:
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"""Get the weather for a given location."""
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# Extract the injected argument from the explicit context
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user_id = ctx.kwargs.get("user_id", "unknown")
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# Simulate using the user_id for logging or personalization
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print(f"Getting weather for user: {user_id}")
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return f"The weather in {location} is cloudy with a high of 15°C."
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async def main() -> None:
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agent = OpenAIResponsesClient().as_agent(
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name="WeatherAgent",
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instructions="You are a helpful weather assistant.",
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tools=[get_weather],
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)
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# Pass the runtime context explicitly when running the agent.
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response = await agent.run(
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"What is the weather like in Amsterdam?",
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function_invocation_kwargs={"user_id": "user_123"},
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)
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print(f"Agent: {response.text}")
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if __name__ == "__main__":
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asyncio.run(main())
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