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# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to create a multi-agent workflow using a synchronous client
with a student agent answering the question first and then a teacher agent checking the answer.
USAGE:
python sample_workflow_multi_agent.py
Before running the sample:
pip install "azure-ai-projects>=2.0.0b1" azure-identity aiohttp
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Microsoft Foundry portal.
2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
the "Models + endpoints" tab in your Microsoft Foundry project.
"""
import os
import json
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
AgentReference,
PromptAgentDefinition,
ResponseStreamEventType,
WorkflowAgentDefinition,
)
load_dotenv()
endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
with (
DefaultAzureCredential() as credential,
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
project_client.get_openai_client() as openai_client,
):
# Create Teacher Agent
teacher_agent = project_client.agents.create_version(
agent_name="teacher-agent",
definition=PromptAgentDefinition(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
instructions="""You are a teacher that create pre-school math question for student and check answer.
If the answer is correct, you stop the conversation by saying [COMPLETE].
If the answer is wrong, you ask student to fix it.""",
),
)
print(f"Agent created (id: {teacher_agent.id}, name: {teacher_agent.name}, version: {teacher_agent.version})")
# Create Student Agent
student_agent = project_client.agents.create_version(
agent_name="student-agent",
definition=PromptAgentDefinition(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
instructions="""You are a student who answers questions from the teacher.
When the teacher gives you a question, you answer it.""",
),
)
print(f"Agent created (id: {student_agent.id}, name: {student_agent.name}, version: {student_agent.version})")
# Create Multi-Agent Workflow
workflow_yaml = f"""
kind: workflow
trigger:
kind: OnConversationStart
id: my_workflow
actions:
- kind: SetVariable
id: set_variable_input_task
variable: Local.LatestMessage
value: "=UserMessage(System.LastMessageText)"
- kind: CreateConversation
id: create_student_conversation
conversationId: Local.StudentConversationId
- kind: CreateConversation
id: create_teacher_conversation
conversationId: Local.TeacherConversationId
- kind: InvokeAzureAgent
id: student_agent
description: The student node
conversationId: "=Local.StudentConversationId"
agent:
name: {student_agent.name}
input:
messages: "=Local.LatestMessage"
output:
messages: Local.LatestMessage
- kind: InvokeAzureAgent
id: teacher_agent
description: The teacher node
conversationId: "=Local.TeacherConversationId"
agent:
name: {teacher_agent.name}
input:
messages: "=Local.LatestMessage"
output:
messages: Local.LatestMessage
- kind: SetVariable
id: set_variable_turncount
variable: Local.TurnCount
value: "=Local.TurnCount + 1"
- kind: ConditionGroup
id: completion_check
conditions:
- condition: '=!IsBlank(Find("[COMPLETE]", Upper(Last(Local.LatestMessage).Text)))'
id: check_done
actions:
- kind: EndConversation
id: end_workflow
- condition: "=Local.TurnCount >= 4"
id: check_turn_count_exceeded
actions:
- kind: SendActivity
id: send_activity_tired
activity: "Let's try again later...I am tired."
elseActions:
- kind: GotoAction
id: goto_student_agent
actionId: student_agent
"""
workflow = project_client.agents.create_version(
agent_name="student-teacherworkflow",
definition=WorkflowAgentDefinition(workflow=workflow_yaml),
)
print(f"Agent created (id: {workflow.id}, name: {workflow.name}, version: {workflow.version})")
conversation = openai_client.conversations.create()
print(f"Created conversation (id: {conversation.id})")
stream = openai_client.responses.create(
conversation=conversation.id,
extra_body={"agent": AgentReference(name=workflow.name).as_dict()},
input="1 + 1 = ?",
stream=True,
metadata={"x-ms-debug-mode-enabled": "1"},
)
for event in stream:
if event.type == ResponseStreamEventType.RESPONSE_OUTPUT_TEXT_DONE:
print("\t", event.text)
elif (
event.type == ResponseStreamEventType.RESPONSE_OUTPUT_ITEM_ADDED
and event.item.type == "workflow_action"
and (event.item.action_id == "teacher_agent" or event.item.action_id == "student_agent")
):
print(f"********************************\nActor - '{event.item.action_id}' :")
# feel free to uncomment below to see more events
# elif event.type == ResponseStreamEventType.RESPONSE_OUTPUT_ITEM_ADDED and event.item.type == "workflow_action":
# print(f"Workflow Item '{event.item.action_id}' is '{event.item.status}' - (previous item was : '{event.item.previous_action_id}')")
# elif event.type == ResponseStreamEventType.RESPONSE_OUTPUT_ITEM_DONE and event.item.type == "workflow_action":
# print(f"Workflow Item '{event.item.action_id}' is '{event.item.status}' - (previous item was: '{event.item.previous_action_id}')")
# elif event.type == ResponseStreamEventType.RESPONSE_OUTPUT_TEXT_DELTA:
# print(event.delta)
openai_client.conversations.delete(conversation_id=conversation.id)
print("Conversation deleted")
project_client.agents.delete_version(agent_name=workflow.name, agent_version=workflow.version)
print("Workflow deleted")
project_client.agents.delete_version(agent_name=student_agent.name, agent_version=student_agent.version)
print("Student Agent deleted")
project_client.agents.delete_version(agent_name=teacher_agent.name, agent_version=teacher_agent.version)
print("Teacher Agent deleted")
# [END create_multi_agent_workflow]
|
