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
|
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to use Agent operations with the Bing Custom Search tool from
the Azure Agents service using a synchronous client.
For more information on the Bing Custom Search tool, see: https://aka.ms/AgentCustomSearchDoc
USAGE:
python sample_agents_bing_custom_search.py
Before running the sample:
pip install azure-ai-agents azure-identity
Set this 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) BING_CUSTOM_CONNECTION_ID - The ID of the Bing Custom Search connection, in the format of:
/subscriptions/{subscription-id}/resourceGroups/{resource-group-name}/providers/Microsoft.MachineLearningServices/workspaces/{workspace-name}/connections/{connection-name}
"""
import os
from azure.ai.agents import AgentsClient
from azure.identity import DefaultAzureCredential
from azure.ai.agents.models import BingCustomSearchTool
# Create an Azure AI Client from a connection string, copied from your AI Studio project.
# At the moment, it should be in the format "<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<HubName>"
# Customer needs to login to Azure subscription via Azure CLI and set the environment variables
agents_client = AgentsClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
conn_id = os.environ["BING_CUSTOM_CONNECTION_ID"]
# Initialize Bing Custom Search tool with connection id and instance name
bing_custom_tool = BingCustomSearchTool(connection_id=conn_id, instance_name="<config_instance_name>")
# Create Agent with the Bing Custom Search tool and process Agent run
with agents_client:
agent = agents_client.create_agent(
model=os.environ["MODEL_DEPLOYMENT_NAME"],
name="my-agent",
instructions="You are a helpful agent",
tools=bing_custom_tool.definitions,
)
print(f"Created agent, ID: {agent.id}")
# Create thread for communication
thread = agents_client.threads.create()
print(f"Created thread, ID: {thread.id}")
# Create message to thread
message = agents_client.messages.create(
thread_id=thread.id,
role="user",
content="How many medals did the USA win in the 2024 summer olympics?",
)
print(f"Created message, ID: {message.id}")
# Create and process Agent run in thread with tools
run = 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}")
# Delete the Agent when done
agents_client.delete_agent(agent.id)
print("Deleted agent")
# Fetch and log all messages
messages = agents_client.messages.list(thread_id=thread.id)
for msg in messages:
if msg.text_messages:
for text_message in msg.text_messages:
print(f"Agent response: {text_message.text.value}")
for annotation in msg.url_citation_annotations:
print(f"URL Citation: [{annotation.url_citation.title}]({annotation.url_citation.url})")
|
