1# ------------------------------------
2# Copyright (c) Microsoft Corporation.
3# Licensed under the MIT License.
4# ------------------------------------
5 
6"""
7DESCRIPTION:
8    This sample demonstrates how to use agent operations with file searching from
9    the Azure Agents service using a synchronous client.
10 
11USAGE:
12    python sample_agents_file_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"""
24 
25import os
26from azure.ai.projects import AIProjectClient
27from azure.ai.agents.models import (
28    FilePurpose,
29    FileSearchTool,
30    ListSortOrder,
31    RunAdditionalFieldList,
32    RunStepFileSearchToolCall,
33    RunStepToolCallDetails,
34)
35from azure.identity import DefaultAzureCredential
36 
37asset_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets/product_info_1.md"))
38 
39project_client = AIProjectClient(
40    endpoint=os.environ["PROJECT_ENDPOINT"],
41    credential=DefaultAzureCredential(),
42)
43 
44with project_client:
45    agents_client = project_client.agents
46 
47    # Upload file and create vector store
48    # [START upload_file_create_vector_store_and_agent_with_file_search_tool]
49    file = agents_client.files.upload_and_poll(file_path=asset_file_path, purpose=FilePurpose.AGENTS)
50    print(f"Uploaded file, file ID: {file.id}")
51 
52    vector_store = agents_client.vector_stores.create_and_poll(file_ids=[file.id], name="my_vectorstore")
53    print(f"Created vector store, vector store ID: {vector_store.id}")
54 
55    # Create file search tool with resources followed by creating agent
56    file_search = FileSearchTool(vector_store_ids=[vector_store.id])
57 
58    agent = agents_client.create_agent(
59        model=os.environ["MODEL_DEPLOYMENT_NAME"],
60        name="my-agent",
61        instructions="Hello, you are helpful agent and can search information from uploaded files",
62        tools=file_search.definitions,
63        tool_resources=file_search.resources,
64    )
65    # [END upload_file_create_vector_store_and_agent_with_file_search_tool]
66 
67    print(f"Created agent, ID: {agent.id}")
68 
69    # Create thread for communication
70    thread = agents_client.threads.create()
71    print(f"Created thread, ID: {thread.id}")
72 
73    # Create message to thread
74    message = agents_client.messages.create(
75        thread_id=thread.id, role="user", content="Hello, what Contoso products do you know?"
76    )
77    print(f"Created message, ID: {message.id}")
78 
79    # Create and process agent run in thread with tools
80    run = agents_client.runs.create_and_process(
81        thread_id=thread.id,
82        agent_id=agent.id,
83    )
84    print(f"Run finished with status: {run.status}")
85 
86    if run.status == "failed":
87        # Check if you got "Rate limit is exceeded.", then you want to get more quota
88        print(f"Run failed: {run.last_error}")
89 
90    # [START teardown]
91    # Delete the file when done
92    agents_client.vector_stores.delete(vector_store.id)
93    print("Deleted vector store")
94 
95    agents_client.files.delete(file_id=file.id)
96    print("Deleted file")
97 
98    # Delete the agent when done
99    agents_client.delete_agent(agent.id)
100    print("Deleted agent")
101    # [END teardown]
102 
103    for run_step in agents_client.run_steps.list(
104        thread_id=thread.id, run_id=run.id, include=[RunAdditionalFieldList.FILE_SEARCH_CONTENTS]
105    ):
106        if isinstance(run_step.step_details, RunStepToolCallDetails):
107            for tool_call in run_step.step_details.tool_calls:
108                if (
109                    isinstance(tool_call, RunStepFileSearchToolCall)
110                    and tool_call.file_search
111                    and tool_call.file_search.results
112                    and tool_call.file_search.results[0].content
113                    and tool_call.file_search.results[0].content[0].text
114                ):
115                    print(
116                        "The search tool has found the next relevant content in "
117                        f"the file {tool_call.file_search.results[0].file_name}:"
118                    )
119                    # Note: technically we may have several search results, however in our example
120                    # we only have one file, so we are taking the only result.
121                    print(tool_call.file_search.results[0].content[0].text)
122                    print("===============================================================")
123 
124    # Fetch and log all messages
125    messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
126 
127    # Print last messages from the thread
128    file_name = os.path.split(asset_file_path)[-1]
129    for msg in messages:
130        if msg.text_messages:
131            last_text = msg.text_messages[-1].text.value
132            for annotation in msg.text_messages[-1].text.annotations:
133                citation = (
134                    file_name if annotation.file_citation.file_id == file.id else annotation.file_citation.file_id
135                )
136                last_text = last_text.replace(annotation.text, f" [{citation}]")
137            print(f"{msg.role}: {last_text}")