1# pylint: disable=line-too-long,useless-suppression
2# ------------------------------------
3# Copyright (c) Microsoft Corporation.
4# Licensed under the MIT License.
5# ------------------------------------
6 
7"""
8DESCRIPTION:
9    This sample demonstrates how to use agent operations with the
10    Azure AI Search tool from the Azure agents service using a synchronous client.
11 
12PREREQUISITES:
13    You will need an Azure AI Search Resource.
14    If you already have one, you must create an agent that can use an existing Azure AI Search index:
15    https://learn.microsoft.com/azure/ai-services/agents/how-to/tools/azure-ai-search?tabs=azurecli%2Cpython&pivots=overview-azure-ai-search
16 
17    If you do not already have an agent Setup with an Azure AI Search resource, follow the guide for a Standard agent setup:
18    https://learn.microsoft.com/azure/ai-services/agents/quickstart?pivots=programming-language-python-azure
19 
20USAGE:
21    python sample_agents_azure_ai_search.py
22 
23    Before running the sample:
24 
25    pip install azure-ai-projects azure-ai-projects azure-identity
26 
27    Set these environment variables with your own values:
28    1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
29                          page of your Azure AI Foundry portal.
30    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
31       the "Models + endpoints" tab in your Azure AI Foundry project.
32    3) AI_SEARCH_CONNECTION_NAME - The connection name of the AI Search connection to your Foundry project,
33       as found under the "Name" column in the "Connected Resources" tab in your Azure AI Foundry project.
34"""
35 
36import os
37from azure.ai.projects import AIProjectClient
38from azure.ai.projects.models import ConnectionType
39from azure.identity import DefaultAzureCredential
40from azure.ai.agents.models import AzureAISearchQueryType, AzureAISearchTool, ListSortOrder, MessageRole
41 
42with AIProjectClient(
43    endpoint=os.environ["PROJECT_ENDPOINT"],
44    credential=DefaultAzureCredential(),
45) as project_client:
46 
47    # [START create_agent_with_azure_ai_search_tool]
48    conn_id = project_client.connections.get_default(ConnectionType.AZURE_AI_SEARCH).id
49 
50    print(conn_id)
51 
52    # Initialize agent AI search tool and add the search index connection id
53    ai_search = AzureAISearchTool(
54        index_connection_id=conn_id,
55        index_name="sample_index",
56        query_type=AzureAISearchQueryType.SIMPLE,
57        top_k=3,
58        filter="",
59    )
60 
61    # Create agent with AI search tool and process agent run
62    agents_client = project_client.agents
63 
64    agent = agents_client.create_agent(
65        model=os.environ["MODEL_DEPLOYMENT_NAME"],
66        name="my-agent",
67        instructions="You are a helpful agent",
68        tools=ai_search.definitions,
69        tool_resources=ai_search.resources,
70    )
71    # [END create_agent_with_azure_ai_search_tool]
72    print(f"Created agent, ID: {agent.id}")
73 
74    # Create thread for communication
75    thread = agents_client.threads.create()
76    print(f"Created thread, ID: {thread.id}")
77 
78    # Create message to thread
79    message = agents_client.messages.create(
80        thread_id=thread.id,
81        role="user",
82        content="What is the temperature rating of the cozynights sleeping bag?",
83    )
84    print(f"Created message, ID: {message.id}")
85 
86    # Create and process agent run in thread with tools
87    run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
88    print(f"Run finished with status: {run.status}")
89 
90    if run.status == "failed":
91        print(f"Run failed: {run.last_error}")
92 
93    # Fetch run steps to get the details of the agent run
94    run_steps = agents_client.run_steps.list(thread_id=thread.id, run_id=run.id)
95    for step in run_steps:
96        print(f"Step {step['id']} status: {step['status']}")
97        step_details = step.get("step_details", {})
98        tool_calls = step_details.get("tool_calls", [])
99 
100        if tool_calls:
101            print("  Tool calls:")
102            for call in tool_calls:
103                print(f"    Tool Call ID: {call.get('id')}")
104                print(f"    Type: {call.get('type')}")
105 
106                azure_ai_search_details = call.get("azure_ai_search", {})
107                if azure_ai_search_details:
108                    print(f"    azure_ai_search input: {azure_ai_search_details.get('input')}")
109                    print(f"    azure_ai_search output: {azure_ai_search_details.get('output')}")
110        print()  # add an extra newline between steps
111 
112    # Delete the agent when done
113    agents_client.delete_agent(agent.id)
114    print("Deleted agent")
115 
116    # [START populate_references_agent_with_azure_ai_search_tool]
117    # Fetch and log all messages
118    messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
119    for message in messages:
120        if message.role == MessageRole.AGENT and message.url_citation_annotations:
121            placeholder_annotations = {
122                annotation.text: f" [see {annotation.url_citation.title}] ({annotation.url_citation.url})"
123                for annotation in message.url_citation_annotations
124            }
125            for message_text in message.text_messages:
126                message_str = message_text.text.value
127                for k, v in placeholder_annotations.items():
128                    message_str = message_str.replace(k, v)
129                print(f"{message.role}: {message_str}")
130        else:
131            for message_text in message.text_messages:
132                print(f"{message.role}: {message_text.text.value}")
133    # [END populate_references_agent_with_azure_ai_search_tool]