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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 grounding tool from
the Azure Agents service using a synchronous client.
USAGE:
python sample_agents_bing_grounding.py
Before running the sample:
pip install azure-ai-agents azure-identity
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_BING_CONNECTION_ID - The ID of the Bing 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.ai.agents.models import MessageRole, BingGroundingTool
from azure.identity import DefaultAzureCredential
agents_client = AgentsClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
# [START create_agent_with_bing_grounding_tool]
conn_id = os.environ["AZURE_BING_CONNECTION_ID"]
# Initialize agent bing tool and add the connection id
bing = BingGroundingTool(connection_id=conn_id)
# Create agent with the bing 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.definitions,
)
# [END create_agent_with_bing_grounding_tool]
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=MessageRole.USER,
content="How does wikipedia explain Euler's Identity?",
)
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}")
# 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)
for step in run_steps:
print(f"Step {step['id']} status: {step['status']}")
step_details = step.get("step_details", {})
tool_calls = step_details.get("tool_calls", [])
if tool_calls:
print(" Tool calls:")
for call in tool_calls:
print(f" Tool Call ID: {call.get('id')}")
print(f" Type: {call.get('type')}")
bing_grounding_details = call.get("bing_grounding", {})
if bing_grounding_details:
print(f" Bing Grounding ID: {bing_grounding_details.get('requesturl')}")
print() # add an extra newline between steps
# Delete the agent when done
agents_client.delete_agent(agent.id)
print("Deleted agent")
# Print the Agent's response message with optional citation
response_message = 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})")
|
