1# pylint: disable=line-too-long,useless-suppression
2# ------------------------------------
3# Copyright (c) Microsoft Corporation.
4# Licensed under the MIT License.
5# ------------------------------------
6"""
7DESCRIPTION:
8    This sample demonstrates how to use agent operations with code interpreter from
9    the Azure Agents service using a synchronous client.
10 
11USAGE:
12    python sample_agents_code_interpreter_attachment_enterprise_search.py
13 
14    Before running the sample:
15 
16    pip install azure-ai-projects azure-ai-agents azure-identity
17 
18    Set these environment variables with your own values:
19    1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
20                          page of your Azure AI Foundry portal.
21    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
22       the "Models + endpoints" tab in your Azure AI Foundry project.
23    3) AZURE_BLOB_URI - The URI of the blob storage where the file is uploaded. In the format:
24         azureml://subscriptions/{subscription-id}/resourcegroups/{resource-group-name}/workspaces/{workspace-name}/datastores/{datastore-name}/paths/{path-to-file}
25"""
26 
27import os
28from azure.ai.projects import AIProjectClient
29from azure.ai.agents.models import (
30    CodeInterpreterTool,
31    MessageAttachment,
32    MessageRole,
33    VectorStoreDataSource,
34    VectorStoreDataSourceAssetType,
35)
36from azure.identity import DefaultAzureCredential
37 
38project_client = AIProjectClient(
39    endpoint=os.environ["PROJECT_ENDPOINT"],
40    credential=DefaultAzureCredential(),
41)
42 
43with project_client:
44    agents_client = project_client.agents
45 
46    code_interpreter = CodeInterpreterTool()
47 
48    # notice that CodeInterpreter must be enabled in the agent creation, otherwise the agent will not be able to see the file attachment
49    agent = agents_client.create_agent(
50        model=os.environ["MODEL_DEPLOYMENT_NAME"],
51        name="my-agent",
52        instructions="You are helpful agent",
53        tools=code_interpreter.definitions,
54    )
55    print(f"Created agent, agent ID: {agent.id}")
56 
57    thread = agents_client.threads.create()
58    print(f"Created thread, thread ID: {thread.id}")
59 
60    # [START upload_file_and_create_message_with_code_interpreter]
61    # We will upload the local file to Azure and will use it for vector store creation.
62    asset_uri = os.environ["AZURE_BLOB_URI"]
63    ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
64 
65    # Create a message with the attachment
66    attachment = MessageAttachment(data_source=ds, tools=code_interpreter.definitions)
67    message = agents_client.messages.create(
68        thread_id=thread.id, role="user", content="What does the attachment say?", attachments=[attachment]
69    )
70    # [END upload_file_and_create_message_with_code_interpreter]
71 
72    print(f"Created message, message ID: {message.id}")
73 
74    run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
75    print(f"Run finished with status: {run.status}")
76 
77    if run.status == "failed":
78        # Check if you got "Rate limit is exceeded.", then you want to get more quota
79        print(f"Run failed: {run.last_error}")
80 
81    agents_client.delete_agent(agent.id)
82    print("Deleted agent")
83 
84    last_msg = agents_client.messages.get_last_message_text_by_role(thread_id=thread.id, role=MessageRole.AGENT)
85    if last_msg:
86        print(f"Last Message: {last_msg.text.value}")