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* restructure: Python samples into progressive 01-05 layout - 01-get-started/: 6 numbered steps (hello agent → hosting) - 02-agents/: all agent concept samples (tools, middleware, providers, etc.) - 03-workflows/: ALL existing workflow samples preserved as-is - 04-hosting/: azure-functions, durabletask, a2a - 05-end-to-end/: demos, evaluation, hosted agents - Old files moved to _to_delete/ for review - Added AGENTS.md with structure documentation - autogen-migration/ and semantic-kernel-migration/ preserved at root * fix: switch to AzureOpenAI Foundry, fix CI failures - Switch all 01-get-started samples to AzureOpenAIResponsesClient with Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT + AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential) - Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes - Fix test paths in packages/ that referenced old getting_started/ dirs: durabletask conftest + streaming test, azurefunctions conftest, devui conftest + capture_messages + openai_sdk_integration - Fix workflow_as_agent_human_in_the_loop.py import (sibling import) - Update hosting READMEs and tool comment paths - Replace root README.md with new structure overview - Update AGENTS.md to document Azure OpenAI Foundry as default provider * cleanup: remove _to_delete folder, copy resource files to active dirs All files in _to_delete/ were either: - Exact duplicates of files in the new structure (240 files) - Same file with only comment path updates (100 files) - One import-fix diff (workflow_as_agent_human_in_the_loop.py) - One superseded minimal_sample.py Resource files (sample.pdf, countries.json, employees.pdf, weather.json) copied to 02-agents/sample_assets/ and 02-agents/resources/ since active samples reference them. * fix: address PR review comments, centralize resources, remove root duplicates - Fix type annotation in 04_memory.py (string union -> proper types) - Fix old sample paths in observability files - Fix grammar/spelling in observability samples - Move sample_assets/ and resources/ to shared/ folder - Remove 8 duplicate observability files from 02-agents root - Update resource path references in multimodal_input and provider samples * fix: update broken links from old getting_started paths to new structure - Update relative paths in READMEs: getting_started/ → 01-get-started/, 02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/ - Fix absolute GitHub URLs in package READMEs - Fix broken link in ollama package README * fix: convert absolute GitHub URLs to relative paths for link checker Absolute URLs to python/samples/ on main branch 404 until PR merges. Converted to relative paths that linkspector can verify locally. * fix: update link for handoff sample moved to orchestrations/ * fix: update chatkit-integration README path from demos/ to 05-end-to-end/ * fix: update broken links in orchestrations README to match flat directory structure
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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"""
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Hello Agent — Simplest possible agent
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This sample creates a minimal agent using AzureOpenAIResponsesClient via an
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Azure AI Foundry project endpoint, and runs it in both non-streaming and streaming modes.
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Environment variables:
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AZURE_AI_PROJECT_ENDPOINT — Your Azure AI Foundry project endpoint
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AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME — Model deployment name (e.g. gpt-4o)
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"""
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async def main() -> None:
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# <create_agent>
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credential = AzureCliCredential()
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
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credential=credential,
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)
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agent = client.as_agent(
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name="HelloAgent",
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instructions="You are a friendly assistant. Keep your answers brief.",
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)
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# </create_agent>
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# <run_agent>
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# Non-streaming: get the complete response at once
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result = await agent.run("What is the capital of France?")
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print(f"Agent: {result}")
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# </run_agent>
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# <run_agent_streaming>
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# Streaming: receive tokens as they are generated
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print("Agent (streaming): ", end="", flush=True)
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async for chunk in agent.run("Tell me a one-sentence fun fact.", stream=True):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print()
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# </run_agent_streaming>
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if __name__ == "__main__":
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asyncio.run(main())
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