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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-projects 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) BING_CONNECTION_NAME - The name of a connection to the Bing resource as it is
listed in Azure AI Foundry connected resources.
"""
import os
from azure.ai.projects import AIProjectClient
from azure.ai.agents.models import MessageRole, BingGroundingTool
from azure.identity import DefaultAzureCredential
project_client = AIProjectClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
# [START create_agent_with_bing_grounding_tool]
conn_id = project_client.connections.get(os.environ["BING_CONNECTION_NAME"]).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 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.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:
responses = []
for text_message in response_message.text_messages:
responses.append(text_message.text.value)
message = " ".join(responses)
for annotation in response_message.url_citation_annotations:
message = message.replace(
annotation.text, f" [{annotation.url_citation.title}]({annotation.url_citation.url})"
)
print(f"Agent response: {message}")
|
