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fd253c0b0e
* Move workflow-samples and agent-samples under declarative-agents and update all references Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/f70f7d19-9256-4eec-b7db-28007d74440c Co-authored-by: sphenry <6749825+sphenry@users.noreply.github.com> * Fix relative paths in README files inside moved directories Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/f70f7d19-9256-4eec-b7db-28007d74440c Co-authored-by: sphenry <6749825+sphenry@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: sphenry <6749825+sphenry@users.noreply.github.com> Co-authored-by: Shawn Henry <shahen@microsoft.com>
45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from pathlib import Path
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from agent_framework.declarative import AgentFactory
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from azure.identity.aio import AzureCliCredential
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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"""
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This sample demonstrates creating an agent from a declarative YAML file specification.
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It uses a MCP server to connect to the Microsoft Learn content and a FoundryChatClient.
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The yaml also has some chat options set, such as temperature and topP.
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These options do not work with newer OpenAI models, so ensure to use a compatible model such as gpt-4o-mini.
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Environment variables:
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- FOUNDRY_PROJECT_ENDPOINT: The endpoint URL for the Foundry project.
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- FOUNDRY_MODEL: The model ID to use for the agent, make sure it is compatible with the chat options specified in
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the yaml, or remove the options.
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"""
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async def main():
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"""Create an agent from a declarative yaml specification and run it."""
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# get the path
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current_path = Path(__file__).parent
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yaml_path = current_path.parent.parent.parent.parent / "declarative-agents" / "agent-samples" / "foundry" / "MicrosoftLearnAgent.yaml"
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# create the agent from the yaml
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async with (
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AzureCliCredential() as credential,
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AgentFactory(client_kwargs={"credential": credential}, safe_mode=False).create_agent_from_yaml_path(
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yaml_path
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) as agent,
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):
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response = await agent.run("How do I create a storage account with private endpoint using bicep?")
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print("Agent response:", response.text)
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if __name__ == "__main__":
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asyncio.run(main())
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