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51 lines
1.7 KiB
Python
51 lines
1.7 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 = (
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current_path.parent.parent.parent.parent
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/ "declarative-agents"
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/ "agent-samples"
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/ "foundry"
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/ "MicrosoftLearnAgent.yaml"
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)
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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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