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# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_agents_fabric.py
DESCRIPTION:
This sample demonstrates how to use Agent operations with the Microsoft Fabric grounding tool from
the Azure Agents service using a synchronous client.
USAGE:
python sample_agents_fabric.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) FABRIC_CONNECTION_ID - The ID of the Fabric 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 FabricTool, ListSortOrder
agents_client = AgentsClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
# [START create_agent_with_fabric_tool]
conn_id = os.environ["FABRIC_CONNECTION_ID"]
print(conn_id)
# Initialize an Agent Fabric tool and add the connection id
fabric = FabricTool(connection_id=conn_id)
# Create an Agent with the Fabric tool and process an 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=fabric.definitions,
)
# [END create_agent_with_fabric_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="user",
content="<User query against Fabric resource>",
)
print(f"Created message, ID: {message.id}")
# Create and process an 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:
last_text = msg.text_messages[-1]
print(f"{msg.role}: {last_text.text.value}")
|
