mirror of
https://github.com/microsoft/agent-framework.git
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Tao Chen
016daf3b98
* First samples 1st batch * Fix sample paths * Fix workflow samples * Fix workflow dependency * Correct env vars * Increase idle timeout * Fix workflows HIL sample * Fix more workflow samples
97 lines
3.7 KiB
Python
97 lines
3.7 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 import Agent, AgentResponseUpdate, Message, WorkflowBuilder
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from agent_framework.foundry import FoundryChatClient
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from azure.identity 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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Step 3: Agents in a workflow with streaming
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This sample creates two agents: a Writer agent creates or edits content, and a Reviewer agent which
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evaluates and provides feedback.
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Purpose:
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Show how to create agents from FoundryChatClient and use them directly in a workflow. Demonstrate
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how agents can be used in a workflow.
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Prerequisites:
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- FOUNDRY_MODEL must be the deployment name of a model in your Foundry project.
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- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
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- Basic familiarity with WorkflowBuilder, executors, edges, events, and streaming runs.
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"""
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async def main():
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"""Build the two node workflow and run it with streaming to observe events."""
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# Create the Azure chat client. AzureCliCredential uses your current az login.
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["FOUNDRY_MODEL"],
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credential=AzureCliCredential(),
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)
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writer_agent = Agent(
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client=client,
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instructions=(
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"You are an excellent content writer. You create new content and edit contents based on the feedback."
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),
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name="writer",
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)
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reviewer_agent = Agent(
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client=client,
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instructions=(
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"You are an excellent content reviewer."
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"Provide actionable feedback to the writer about the provided content."
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"Provide the feedback in the most concise manner possible."
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),
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name="reviewer",
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)
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# Build the workflow using the fluent builder.
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# Set the start node via constructor and connect an edge from writer to reviewer.
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workflow = WorkflowBuilder(start_executor=writer_agent).add_edge(writer_agent, reviewer_agent).build()
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# Track the last author to format streaming output.
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last_author: str | None = None
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# Run the workflow with the user's initial message and stream events as they occur.
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async for event in workflow.run(
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Message("user", ["Create a slogan for a new electric SUV that is affordable and fun to drive."]),
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stream=True,
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):
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# The outputs of the workflow are whatever the agents produce. So the events are expected to
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# contain `AgentResponseUpdate` from the agents in the workflow.
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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update = event.data
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author = update.author_name
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if author != last_author:
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if last_author is not None:
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print() # Newline between different authors
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print(f"{author}: {update.text}", end="", flush=True)
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last_author = author
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else:
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print(update.text, end="", flush=True)
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"""
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writer: "Electrify Your Journey: Affordable Fun Awaits!"
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reviewer: Feedback:
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1. **Clarity**: Consider simplifying the message. "Affordable Fun" could be more direct.
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2. **Emotional Appeal**: Emphasize the thrill of driving more. Try using words that evoke excitement.
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3. **Unique Selling Proposition**: Highlight the electric aspect more boldly.
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Example revision: "Charge Your Adventure: Affordable SUVs for Fun-Loving Drivers!"
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"""
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if __name__ == "__main__":
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asyncio.run(main())
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