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* Python: Provider-leading client design & OpenAI package extraction Major refactoring of the Python Agent Framework client architecture: - Extract OpenAI clients into new `agent-framework-openai` package - Core package no longer depends on openai, azure-identity, azure-ai-projects - Rename clients for discoverability: OpenAIResponsesClient → OpenAIChatClient, OpenAIChatClient → OpenAIChatCompletionClient - Unify `model_id`/`deployment_name`/`model_deployment_name` → `model` param - New FoundryChatClient for Azure AI Foundry Responses API - New FoundryAgent/FoundryAgentClient for connecting to pre-configured Foundry agents - Remove OpenAIBase/OpenAIConfigMixin from non-deprecated client MRO - Deprecate AzureOpenAI* clients, AzureAIClient, OpenAIAssistantsClient - Reorganize samples: azure_openai+azure_ai+azure_ai_agent → azure/ - ADR-0020: Provider-Leading Client Design Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: missing Agent imports in samples, .model_id → .model in foundry_local sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: CI failures — mypy errors, coverage targets, sample imports - azure-ai mypy: add type ignores for TypedDict total=, model arg, forward ref - Coverage: replace core.azure/openai targets with openai package target - project_provider: add type annotation for opts dict Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: populate openai .pyi stub, fix broken README links, coverage targets Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fixes * updated observabilitty * reset azure init.pyi * fix errors * updated adr number * fix foundry local * fixed not renamed docstrings and comments, and added deprecated markers to old classes * fix tests and pyprojects * fix test vars * updated function tests * update durable * updated test setup for functions * Fix Foundry auth in workflow samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Stabilize Python integration workflows Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Update hosting samples for Foundry Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger full CI rerun Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger CI rerun again Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * trigger rerun * trigger rerun * fix for litellm * undo durabletask changes * Move Foundry APIs into foundry namespace Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix Foundry pyproject formatting Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Split provider samples by Foundry surface Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Restore hosting sample requirements Also fix the Foundry Local sample link after the provider sample move. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated tests * udpated foundry integration tests * removed dist from azurefunctions tests * Use separate Foundry clients for concurrent agents Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix client setup in azfunc and durable * disabled two tests * updated setup for some function and durable tests * improved azure openai setup with new clients * ignore deprecated * fixes * skip 11 * remove openai assistants int tests --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
133 lines
4.4 KiB
Python
133 lines
4.4 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""
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Demonstrate a workflow that responds to user input using an agent with
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function tools assigned. Exits the loop when the user enters "exit".
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"""
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import asyncio
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import os
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Annotated, Any
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from agent_framework import Agent, FileCheckpointStorage, tool
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from agent_framework.foundry import FoundryChatClient
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from agent_framework_declarative import ExternalInputRequest, ExternalInputResponse, WorkflowFactory
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from azure.identity import AzureCliCredential
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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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TEMP_DIR = Path(__file__).with_suffix("").parent / "tmp" / "checkpoints"
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TEMP_DIR.mkdir(parents=True, exist_ok=True)
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@dataclass
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class MenuItem:
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category: str
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name: str
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price: float
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is_special: bool = False
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MENU_ITEMS = [
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MenuItem(category="Soup", name="Clam Chowder", price=4.95, is_special=True),
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MenuItem(category="Soup", name="Tomato Soup", price=4.95, is_special=False),
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MenuItem(category="Salad", name="Cobb Salad", price=9.99, is_special=False),
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MenuItem(category="Salad", name="House Salad", price=4.95, is_special=False),
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MenuItem(category="Drink", name="Chai Tea", price=2.95, is_special=True),
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MenuItem(category="Drink", name="Soda", price=1.95, is_special=False),
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]
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# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
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# see samples/02-agents/tools/function_tool_with_approval.py
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# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
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@tool(approval_mode="never_require")
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def get_menu() -> list[dict[str, Any]]:
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"""Get all menu items."""
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return [{"category": i.category, "name": i.name, "price": i.price} for i in MENU_ITEMS]
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@tool(approval_mode="never_require")
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def get_specials() -> list[dict[str, Any]]:
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"""Get today's specials."""
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return [{"category": i.category, "name": i.name, "price": i.price} for i in MENU_ITEMS if i.is_special]
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@tool(approval_mode="never_require")
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def get_item_price(name: Annotated[str, Field(description="Menu item name")]) -> str:
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"""Get price of a menu item."""
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for item in MENU_ITEMS:
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if item.name.lower() == name.lower():
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return f"${item.price:.2f}"
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return f"Item '{name}' not found."
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async def main():
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# Create agent with tools
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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menu_agent = Agent(
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client=client,
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name="MenuAgent",
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instructions="Answer questions about menu items, specials, and prices.",
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tools=[get_menu, get_specials, get_item_price],
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)
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# Clean up any existing checkpoints
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for file in TEMP_DIR.glob("*"):
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file.unlink()
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factory = WorkflowFactory(checkpoint_storage=FileCheckpointStorage(TEMP_DIR))
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factory.register_agent("MenuAgent", menu_agent)
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workflow = factory.create_workflow_from_yaml_path(Path(__file__).parent / "workflow.yaml")
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# Get initial input
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print("Restaurant Menu Assistant (type 'exit' to quit)\n")
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user_input = input("You: ").strip() # noqa: ASYNC250
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if not user_input:
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return
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# Run workflow with external loop handling
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pending_request_id: str | None = None
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first_response = True
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while True:
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if pending_request_id:
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response = ExternalInputResponse(user_input=user_input)
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stream = workflow.run(stream=True, responses={pending_request_id: response})
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else:
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stream = workflow.run({"userInput": user_input}, stream=True)
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pending_request_id = None
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first_response = True
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async for event in stream:
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if event.type == "output" and isinstance(event.data, str):
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if first_response:
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print("MenuAgent: ", end="")
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first_response = False
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print(event.data, end="", flush=True)
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elif event.type == "request_info" and isinstance(event.data, ExternalInputRequest):
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pending_request_id = event.request_id
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print()
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if not pending_request_id:
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break
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user_input = input("\nYou: ").strip()
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if not user_input:
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continue
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if __name__ == "__main__":
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asyncio.run(main())
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