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Microsoft Foundry Quickstart

This quickstart will help you get started with Microsoft Foundry by creating a project, deploying a model, running chat completions, and creating an agent with file search capabilities.

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Prerequisites

  • An Azure subscription (Create one for free)
  • Azure CLI installed and authenticated
  • Python 3.8 or later (for Python examples)

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Step 1: Create a Foundry Project

A Foundry project is where you do most of your development work. Projects provide isolation for agents, models, and data.
1

Sign in to Azure

az login
2

Create a Foundry resource and project

You can create resources using the Azure portal or deploy using Azure CLI with Bicep templates.For a basic setup:
# Deploy basic agent setup
az deployment group create \
  --resource-group <your-resource-group> \
  --template-uri https://raw.githubusercontent.com/azure-ai-foundry/foundry-samples/main/infrastructure/infrastructure-setup-bicep/40-basic-agent-setup/azuredeploy.json
This creates:
  • Foundry account
  • Foundry project
  • GPT-4.1 model deployment
3

Get your project endpoint

After deployment, navigate to your project in the Azure portal and copy the endpoint URL:
https://<resource-name>.services.ai.azure.com/api/projects/<project-name>

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Step 2: Install the SDK

pip install openai azure-identity azure-ai-projects==1.0.0

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Step 3: Chat with a Model

Create your first chat completion using a deployed model. 
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential

# Initialize project client
project = AIProjectClient(
    endpoint="https://<resource-name>.services.ai.azure.com/api/projects/<project-name>",
    credential=DefaultAzureCredential(),
)

# Get OpenAI client
models = project.get_openai_client(api_version="2024-10-21")

# Create chat completion
response = models.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "What is the size of France in square miles?"},
    ],
)

print(response.choices[0].message.content)

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Step 4: Create an Agent

Create an intelligent agent that can assist with tasks. 
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
import os
import time

# Initialize client
project = AIProjectClient(
    endpoint=os.environ["PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

# Create agent
agent = project.agents.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful assistant",
)
print(f"Created agent, ID: {agent.id}")

# Create thread
thread = project.agents.threads.create()
print(f"Created thread, ID: {thread.id}")

# Send message
message = project.agents.messages.create(
    thread_id=thread.id,
    role="user",
    content="Hello, what can you help me with?",
)

# Run agent
run = project.agents.runs.create(thread_id=thread.id, agent_id=agent.id)

# Wait for completion
while run.status in ["queued", "in_progress"]:
    time.sleep(1)
    run = project.agents.runs.get(thread_id=thread.id, run_id=run.id)

print(f"Run completed with status: {run.status}")

# Get messages
messages = project.agents.messages.list(thread_id=thread.id)
for msg in messages:
    print(f"{msg['role']}: {msg['content']}")

# Cleanup
project.agents.delete_agent(agent.id)

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Step 5: Add File Search to Your Agent

Enhance your agent with knowledge retrieval capabilities. 
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import FileSearchTool, FilePurpose
from azure.identity import DefaultAzureCredential
import os

project = AIProjectClient(
    endpoint=os.environ["PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

# Upload file
file = project.agents.files.upload_and_poll(
    file_path="product_info_1.md",
    purpose=FilePurpose.AGENTS
)
print(f"Uploaded file, file ID: {file.id}")

# Create file search tool with file
file_search_tool = FileSearchTool(file_ids=[file.id])

# Create agent with file search
agent = project.agents.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="file-search-agent",
    instructions="You are a helpful assistant that can search information from uploaded files.",
    tools=file_search_tool.definitions,
    tool_resources=file_search_tool.resources,
)

print(f"Created agent with file search, agent ID: {agent.id}")

# Create thread and ask question
thread = project.agents.threads.create()
message = project.agents.messages.create(
    thread_id=thread.id,
    role="user",
    content="What products does Contoso offer?",
)

# Run and wait for completion
run = project.agents.runs.create_and_poll(
    thread_id=thread.id,
    agent_id=agent.id
)

if run.status == "completed":
    messages = project.agents.messages.list(thread_id=thread.id)
    for msg in messages:
        print(f"{msg['role']}: {msg['content']}")

# Cleanup
project.agents.delete_agent(agent.id)
project.agents.files.delete(file.id)

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Next Steps

Now that you’ve created your first agent with file search, explore more capabilities:

Agent Overview

Learn about Foundry Agent Service capabilities

Environment Setup

Configure your agent environment

Agent Tools

Explore built-in tools for agents

SDK Overview

Deep dive into the Foundry SDK

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Clean Up Resources

To avoid incurring charges, delete the resources you created: 
az group delete --name <your-resource-group> --yes --no-wait

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