🍏 Observability & Tracing Demo with azure-ai-projects and azure-ai-inference 🍎¶

import os
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.inference.models import UserMessage
from pathlib import Path  # For cross-platform path handling

# Get the path to the .env file which is in the parent directory
notebook_path = Path().absolute()  # Get absolute path of current notebook
parent_dir = notebook_path.parent  # Get parent directory
load_dotenv(parent_dir / '.env')  # Load environment variables from .env file

connection_string = os.environ.get("PROJECT_CONNECTION_STRING")
if not connection_string:
    raise ValueError("🚨 PROJECT_CONNECTION_STRING not found in environment. Please set it in your .env.")

try:
    # Create the AIProjectClient
    project_client = AIProjectClient.from_connection_string(
        credential=DefaultAzureCredential(),
        conn_str=connection_string
    )
    print("✅ Successfully created AIProjectClient")
    
    # Get chat completions client and make request
    with project_client.inference.get_chat_completions_client() as inference_client:
        response = inference_client.complete(
            model=os.environ.get("MODEL_DEPLOYMENT_NAME", "gpt-4o"),  # Get model name from env or use default
            messages=[UserMessage(content="How many feet are in a mile?")]
        )
        print("💡 Response:")
        print(response.choices[0].message.content)

except Exception as e:
    print("❌ Failed to initialize client or get response:", e)

from azure.ai.projects.models import ConnectionType

with project_client:
    # List all connections
    all_conns = project_client.connections.list()
    print(f"🔎 Found {len(all_conns)} total connections.")
    for idx, c in enumerate(all_conns):
        print(f"{idx+1}) Name: {c.name}, Type: {c.type}, IsDefault: {c.is_default}")

    # Filter for Azure OpenAI type, as an example
    aoai_conns = project_client.connections.list(connection_type=ConnectionType.AZURE_OPEN_AI)
    print(f"\n🌀 Found {len(aoai_conns)} Azure OpenAI connections:")
    for c in aoai_conns:
        print(f"   -> {c.name}")

    # Get the default Azure AI Services connection
    default_conn = project_client.connections.get_default(
        connection_type=ConnectionType.AZURE_AI_SERVICES,
        include_credentials=False
    )
    if default_conn:
        print("\n⭐ Default Azure AI Services connection:")
        print(default_conn)
    else:
        print("No default connection found for Azure AI Services.")

import sys
import os
from azure.ai.inference.models import UserMessage
from opentelemetry import trace

# We'll enable local console tracing so we can see the telemetries in our terminal
project_client.telemetry.enable(destination=sys.stdout)

# We'll do a small LLM call example:
try:
    with project_client.inference.get_chat_completions_client() as client:
        prompt_msg = "I'd like to start a simple home workout routine. Any tips?"
        response = client.complete(
            model=os.environ.get("MODEL_DEPLOYMENT_NAME", "some-deployment-name"),
            messages=[UserMessage(content=prompt_msg)]
        )
        print("\n🤖 Response:", response.choices[0].message.content)
except Exception as exc:
    print(f"❌ Chat Completions example failed: {exc}")

import os
from azure.monitor.opentelemetry import configure_azure_monitor
from azure.ai.inference.models import UserMessage

# Enable Azure Monitor tracing if available
connection_str = project_client.telemetry.get_connection_string()
if connection_str:
    print("🔧 Found App Insights connection string. Configuring...")
    configure_azure_monitor(connection_string=connection_str)
    project_client.telemetry.enable()  # add optional additional instrumentations
    
    # We'll do a test chat call again, which should get logged to Azure Monitor
    try:
        with project_client.inference.get_chat_completions_client() as client:
            prompt_msg = "Any low-impact exercises recommended for knee issues?"
            response = client.complete(
                model=os.environ.get("MODEL_DEPLOYMENT_NAME", "some-deployment-name"),
                messages=[UserMessage(content=prompt_msg)]
            )
            print("\n🤖 Response (logged to App Insights):", response.choices[0].message.content)
    except Exception as exc:
        print(f"❌ Chat Completions with Azure Monitor example failed: {exc}")
else:
    print("No Application Insights connection string is configured in this project.")