1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
|
# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to use agent operations with the Browser Automation tool from
the Azure Agents service using an asynchronous client.
USAGE:
python sample_agents_browser_automation_async.py
Before running the sample:
pip install azure-ai-agents --pre
pip install azure-ai-projects azure-identity aiohttp
Set these environment variables with your own values:
1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Azure AI Foundry portal.
2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
the "Models + endpoints" tab in your Azure AI Foundry project.
3) AZURE_PLAYWRIGHT_CONNECTION_ID - The ID of the Azure Playwright Workspaces connection, in the format of:
/subscriptions/{subscription-id}/resourceGroups/{resource-group-name}/providers/Microsoft.MachineLearningServices/workspaces/{workspace-name}/connections/{connection-name}
You can also get the connection ID programmatically from AIProjectClient, using the call
`project_client.connections.get("<playwright-connection-name>").id`.
"""
import os
import asyncio
from azure.ai.projects.aio import AIProjectClient
from azure.ai.agents.models import (
MessageRole,
RunStepToolCallDetails,
BrowserAutomationTool,
RunStepBrowserAutomationToolCall,
)
from azure.identity.aio import DefaultAzureCredential
async def main() -> None:
project_client = AIProjectClient(endpoint=os.environ["PROJECT_ENDPOINT"], credential=DefaultAzureCredential())
async with project_client:
connection_id = os.environ["AZURE_PLAYWRIGHT_CONNECTION_ID"]
# Initialize Browser Automation tool and add the connection id
browser_automation = BrowserAutomationTool(connection_id=connection_id)
agents_client = project_client.agents
# Create a new Agent that has the Browser Automation tool attached.
# Note: To add Browser Automation tool to an existing Agent with an `agent_id`, do the following:
# agent = await agents_client.update_agent(agent_id, tools=browser_automation.definitions)
agent = await agents_client.create_agent(
model=os.environ["MODEL_DEPLOYMENT_NAME"],
name="my-agent",
instructions="""
You are an Agent helping with browser automation tasks.
You can answer questions, provide information, and assist with various tasks
related to web browsing using the Browser Automation tool available to you.
""",
tools=browser_automation.definitions,
)
print(f"Created agent, ID: {agent.id}")
# Create thread for communication
thread = await agents_client.threads.create()
print(f"Created thread, ID: {thread.id}")
# Create message to thread
message = await agents_client.messages.create(
thread_id=thread.id,
role=MessageRole.USER,
content="""
Your goal is to report the percent of Microsoft year-to-date stock price change.
To do that, go to the website finance.yahoo.com.
At the top of the page, you will find a search bar.
Enter the value 'MSFT', to get information about the Microsoft stock price.
At the top of the resulting page you will see a default chart of Microsoft stock price.
Click on 'YTD' at the top of that chart, and report the percent value that shows up just below it.
""",
)
print(f"Created message, ID: {message.id}")
# Create and process agent run in thread with tools
print(f"Waiting for Agent run to complete. Please wait...")
run = await agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
print(f"Run finished with status: {run.status}")
if run.status == "failed":
print(f"Run failed: {run.last_error}")
# Fetch run steps to get the details of the agent run
run_steps = agents_client.run_steps.list(thread_id=thread.id, run_id=run.id)
async for step in run_steps:
print(f"Step {step.id} status: {step.status}")
if isinstance(step.step_details, RunStepToolCallDetails):
print(" Tool calls:")
tool_calls = step.step_details.tool_calls
for call in tool_calls:
print(f" Tool call ID: {call.id}")
print(f" Tool call type: {call.type}")
if isinstance(call, RunStepBrowserAutomationToolCall):
print(f" Browser automation input: {call.browser_automation.input}")
print(f" Browser automation output: {call.browser_automation.output}")
print(" Steps:")
for tool_step in call.browser_automation.steps:
print(f" Last step result: {tool_step.last_step_result}")
print(f" Current state: {tool_step.current_state}")
print(f" Next step: {tool_step.next_step}")
print() # add an extra newline between tool steps
print() # add an extra newline between tool calls
print() # add an extra newline between run steps
# Optional: Delete the agent once the run is finished.
# Comment out this line if you plan to reuse the agent later.
await agents_client.delete_agent(agent.id)
print("Deleted agent")
# Print the Agent's response message with optional citation
response_message = await agents_client.messages.get_last_message_by_role(
thread_id=thread.id, role=MessageRole.AGENT
)
if response_message:
for text_message in response_message.text_messages:
print(f"Agent response: {text_message.text.value}")
for annotation in response_message.url_citation_annotations:
print(f"URL Citation: [{annotation.url_citation.title}]({annotation.url_citation.url})")
if __name__ == "__main__":
asyncio.run(main())
|
