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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 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-identity
pip install --pre azure-ai-projects
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_NAME - The name of a connection to the custom search Bing resource as it is
listed in Azure AI Foundry connected resources.
4) BING_CONFIGURATION_NAME - the name of a search configuration in Grounding with Bing Custom Search
resource.
"""
import os
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
from azure.ai.agents.models import BingCustomSearchTool, ListSortOrder
project_client = AIProjectClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
conn_id = project_client.connections.get(os.environ["BING_CUSTOM_CONNECTION_NAME"]).id
# Initialize Bing Custom Search tool with connection id and instance name
bing_custom_tool = BingCustomSearchTool(connection_id=conn_id, instance_name=os.environ["BING_CONFIGURATION_NAME"])
# Create Agent with the Bing Custom Search tool and process Agent run
with project_client:
agents_client = project_client.agents
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, order=ListSortOrder.ASCENDING)
for msg in messages:
if msg.text_messages:
responses = []
for text_message in msg.text_messages:
responses.append(text_message.text.value)
message = " ".join(responses)
for annotation in msg.url_citation_annotations:
message = message.replace(
annotation.text, f" [{annotation.url_citation.title}]({annotation.url_citation.url})"
)
print(f"{msg.role}: {message}")
|
