AI-103 CERTIFICATION BOOTCAMP

pip install "azure-ai-projects>=2.3.0" azure-identity
az login

# From the project Home page. Never put the API key here.
export FOUNDRY_PROJECT_ENDPOINT="https://<resource-name>.services.ai.azure.com/api/projects/<project-name>"
# The DEPLOYMENT name you chose, not the catalog model name
export FOUNDRY_MODEL_NAME="<your-deployment-name>"

import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition

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

# Create (or add a new version of) a prompt agent
agent = project.agents.create_version(
    agent_name="helpdesk-agent",
    definition=PromptAgentDefinition(
        model=os.environ["FOUNDRY_MODEL_NAME"],  # your deployment name
        instructions="You answer IT helpdesk questions in three sentences or fewer.",
    ),
)
print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")

# Get an OpenAI client pre-bound to the agent, and hold a two-turn conversation
openai = project.get_openai_client(agent_name=agent.name)
conversation = openai.conversations.create()

response = openai.responses.create(
    conversation=conversation.id,
    input="My laptop will not join the office Wi-Fi. What should I check first?",
)
print(response.output_text)

response = openai.responses.create(
    conversation=conversation.id,
    input="I checked that. What next?",
)
print(response.output_text)

Agent created (id: <agent-id>, name: helpdesk-agent, version: 1)
<first answer from your model>
<follow-up answer that builds on the first>

# Python 3.10 or later. A virtual environment keeps course packages apart.
python -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate
pip install "azure-ai-projects>=2.3.0" azure-identity openai
pip show azure-ai-projects       # note the version for your evidence

az login
az account show                  # right subscription and tenant?

export FOUNDRY_PROJECT_ENDPOINT="https://<resource-name>.services.ai.azure.com/api/projects/<project-name>"
export FOUNDRY_MODEL_NAME="<your-llm-deployment-name>"
# My own variable names for the second model (not Microsoft's):
export FOUNDRY_OPENAI_BASE_URL="https://<resource-name>.openai.azure.com/openai/v1/"
export SMALL_MODEL_DEPLOYMENT="<your-small-model-deployment-name>"
# PowerShell equivalent: $env:FOUNDRY_PROJECT_ENDPOINT = "https://..."

import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

# Format: https://<resource-name>.services.ai.azure.com/api/projects/<project-name>
project = AIProjectClient(
    endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

# The OpenAI-compatible client for this project (Responses API, agents, evaluations)
openai = project.get_openai_client()

response = openai.responses.create(
    model=os.environ["FOUNDRY_MODEL_NAME"],  # deployment name, not catalog name
    input="In one sentence, what does a Foundry project endpoint identify?",
)
if not response.output_text or not response.output_text.strip():
    raise RuntimeError("Response output text was empty.")

print(f"Response output: {response.output_text}")

# Keep a stateless multi-turn thread going without an agent
follow_up = openai.responses.create(
    model=os.environ["FOUNDRY_MODEL_NAME"],
    previous_response_id=response.id,
    input="Now say it for a finance manager, in plain English.",
)
print(f"Follow-up: {follow_up.output_text}")

Response output: <one sentence from your model about the project endpoint>
Follow-up: <the same idea, rewritten for a finance manager>

import base64
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

project = AIProjectClient(
    endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)
openai = project.get_openai_client()

# Any photo of your own that contains nothing private
with open("photo.jpg", "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode("utf-8")

response = openai.responses.create(
    model=os.environ["FOUNDRY_MODEL_NAME"],  # a deployment whose model accepts images
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": "What is in this image? Two sentences."},
                {"type": "input_image", "image_url": f"data:image/jpeg;base64,{base64_image}"},
            ],
        }
    ],
)
print(response.output_text)

<two sentences describing your photo>