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# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to use agent operations with file searching from
the Azure Agents service using a synchronous client.
USAGE:
python sample_agents_file_search.py
Before running the sample:
pip install 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.
"""
import os
from azure.ai.agents import AgentsClient
from azure.ai.agents.models import (
FileSearchTool,
FilePurpose,
ListSortOrder,
)
from azure.identity import DefaultAzureCredential
asset_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets/product_info_1.md"))
agents_client = AgentsClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
with agents_client:
# Upload file and create vector store
# [START upload_file_create_vector_store_and_agent_with_file_search_tool]
file = agents_client.files.upload_and_poll(file_path=asset_file_path, purpose=FilePurpose.AGENTS)
print(f"Uploaded file, file ID: {file.id}")
vector_store = agents_client.vector_stores.create_and_poll(file_ids=[file.id], name="my_vectorstore")
print(f"Created vector store, vector store ID: {vector_store.id}")
# Create file search tool with resources followed by creating agent
file_search = FileSearchTool(vector_store_ids=[vector_store.id])
agent = agents_client.create_agent(
model=os.environ["MODEL_DEPLOYMENT_NAME"],
name="my-agent",
instructions="Hello, you are helpful agent and can search information from uploaded files",
tools=file_search.definitions,
tool_resources=file_search.resources,
)
# [END upload_file_create_vector_store_and_agent_with_file_search_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="Hello, what Contoso products do you know?"
)
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":
# Check if you got "Rate limit is exceeded.", then you want to get more quota
print(f"Run failed: {run.last_error}")
# [START teardown]
# Delete the file when done
agents_client.vector_stores.delete(vector_store.id)
print("Deleted vector store")
agents_client.files.delete(file_id=file.id)
print("Deleted file")
# Delete the agent when done
agents_client.delete_agent(agent.id)
print("Deleted agent")
# [END teardown]
# Fetch and log all messages
messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
# Print last messages from the thread
for msg in messages:
if msg.text_messages:
last_text = msg.text_messages[-1]
print(f"{msg.role}: {last_text.text.value}")
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