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Convert observability samples to notebooks
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‎lab/5-Observability/1-OpenAIAgents/weekend_planner.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Weekend Planner with OpenAI Agents Telemetry\n",
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"\n",
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"Capture GenAI-compliant spans while orchestrating an OpenAI Agents workflow. This notebook mirrors the sample script but keeps everything inline so you can tweak the code and immediately inspect telemetry."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Requirements\n",
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"Install the supporting packages before running the cells below.\n",
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"\n",
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"```bash\n",
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"pip install openai openai-agents azure-identity rich python-dotenv\n",
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"pip install opentelemetry-instrumentation-openai-agents-v2\n",
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"# Optional Azure Monitor exporter\n",
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"pip install azure-monitor-opentelemetry-exporter\n",
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"```\n",
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"\n",
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"Set environment variables for your model provider (`API_HOST=github` by default) and, if needed, `APPLICATION_INSIGHTS_CONNECTION_STRING`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import annotations\n",
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"\n",
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"import asyncio\n",
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"import logging\n",
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"import os\n",
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"import random\n",
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"from dataclasses import dataclass\n",
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"from datetime import datetime\n",
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"from typing import Callable\n",
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"from urllib.parse import urlparse\n",
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"\n",
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"import azure.identity\n",
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"import openai\n",
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"from agents import Agent, OpenAIChatCompletionsModel, Runner, function_tool, set_tracing_disabled\n",
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"from dotenv import load_dotenv\n",
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"from rich.logging import RichHandler\n",
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"\n",
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"from opentelemetry import trace\n",
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"from opentelemetry.instrumentation.openai_agents import OpenAIAgentsInstrumentor\n",
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"from opentelemetry.sdk.resources import Resource\n",
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"from opentelemetry.sdk.trace import TracerProvider\n",
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"from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter\n",
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"\n",
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"try:\n",
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" from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n",
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"except ImportError: # pragma: no cover - optional dependency\n",
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" AzureMonitorTraceExporter = None\n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"logging.basicConfig(level=logging.WARNING, format=\"%(message)s\", datefmt=\"[%X]\", handlers=[RichHandler()])\n",
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"LOGGER = logging.getLogger(\"weekend_planner\")\n",
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"LOGGER.setLevel(logging.INFO)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"@dataclass\n",
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"class _ApiConfig:\n",
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" \"\"\"Helper describing how to create the OpenAI client.\"\"\"\n",
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"\n",
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" build_client: Callable[[], object]\n",
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" model_name: str\n",
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" base_url: str\n",
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" provider: str\n",
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"\n",
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"\n",
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"def _set_capture_env(provider: str, base_url: str) -> None:\n",
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" \"\"\"Enable GenAI capture toggles required by the instrumentation layer.\"\"\"\n",
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"\n",
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" capture_defaults = {\n",
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" \"OTEL_INSTRUMENTATION_OPENAI_AGENTS_CAPTURE_CONTENT\": \"true\",\n",
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" \"OTEL_INSTRUMENTATION_OPENAI_AGENTS_CAPTURE_METRICS\": \"true\",\n",
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" \"OTEL_GENAI_CAPTURE_MESSAGES\": \"true\",\n",
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" \"OTEL_GENAI_CAPTURE_SYSTEM_INSTRUCTIONS\": \"true\",\n",
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" \"OTEL_GENAI_CAPTURE_TOOL_DEFINITIONS\": \"true\",\n",
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" \"OTEL_GENAI_EMIT_OPERATION_DETAILS\": \"true\",\n",
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" \"OTEL_GENAI_AGENT_NAME\": os.getenv(\"OTEL_GENAI_AGENT_NAME\", \"Weekend Planner Agent\"),\n",
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" \"OTEL_GENAI_AGENT_DESCRIPTION\": os.getenv(\"OTEL_GENAI_AGENT_DESCRIPTION\", \"Assistant that plans weekend activities using weather and events data\"),\n",
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" \"OTEL_GENAI_AGENT_ID\": os.getenv(\"OTEL_GENAI_AGENT_ID\", \"weekend-planner\"),\n",
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" \"OTEL_GENAI_PROVIDER_NAME\": provider,\n",
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" }\n",
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" for key, value in capture_defaults.items():\n",
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" os.environ.setdefault(key, value)\n",
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"\n",
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" parsed = urlparse(base_url)\n",
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" if parsed.hostname:\n",
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" os.environ.setdefault(\"OTEL_GENAI_SERVER_ADDRESS\", parsed.hostname)\n",
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" if parsed.port:\n",
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" os.environ.setdefault(\"OTEL_GENAI_SERVER_PORT\", str(parsed.port))\n",
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"\n",
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"\n",
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"def _resolve_api_config() -> _ApiConfig:\n",
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" \"\"\"Return the client configuration for the requested host.\"\"\"\n",
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"\n",
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" host = os.getenv(\"API_HOST\", \"github\").lower()\n",
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"\n",
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" if host == \"github\":\n",
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" base_url = os.getenv(\"GITHUB_OPENAI_BASE_URL\", \"https://models.inference.ai.azure.com\").rstrip(\"/\")\n",
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" model_name = os.getenv(\"GITHUB_MODEL\", \"gpt-4o\")\n",
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" api_key = os.environ[\"GITHUB_TOKEN\"]\n",
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"\n",
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" def _build_client() -> openai.AsyncOpenAI:\n",
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" return openai.AsyncOpenAI(base_url=base_url, api_key=api_key)\n",
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"\n",
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" return _ApiConfig(build_client=_build_client, model_name=model_name, base_url=base_url, provider=\"azure.ai.inference\")\n",
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"\n",
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" if host == \"azure\":\n",
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" endpoint = os.environ[\"AZURE_OPENAI_ENDPOINT\"].rstrip(\"/\")\n",
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" api_version = os.environ[\"AZURE_OPENAI_VERSION\"]\n",
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" deployment = os.environ[\"AZURE_OPENAI_CHAT_DEPLOYMENT\"]\n",
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"\n",
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" credential = azure.identity.DefaultAzureCredential()\n",
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" token_provider = azure.identity.get_bearer_token_provider(credential, \"https://cognitiveservices.azure.com/.default\")\n",
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"\n\n",
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" def _build_client() -> openai.AsyncAzureOpenAI:\n",
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" return openai.AsyncAzureOpenAI(api_version=api_version, azure_endpoint=endpoint, azure_ad_token_provider=token_provider)\n",
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"\n",
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" return _ApiConfig(build_client=_build_client, model_name=deployment, base_url=endpoint, provider=\"azure.ai.openai\")\n",
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"\n",
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" raise ValueError(\"Unsupported API_HOST. Expected 'github' or 'azure'.\")\n",
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"\n",
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"\n",
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"def _configure_tracer() -> None:\n",
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" \"\"\"Configure tracer provider and exporter.\"\"\"\n",
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"\n",
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" resource = Resource.create({\"service.name\": \"weekend-planner-service\", \"service.namespace\": \"ignite25\", \"service.version\": os.getenv(\"SERVICE_VERSION\", \"1.0.0\")})\n",
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" provider = TracerProvider(resource=resource)\n",
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" connection_string = os.getenv(\"APPLICATION_INSIGHTS_CONNECTION_STRING\")\n",
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"\n",
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" if connection_string and AzureMonitorTraceExporter is not None:\n",
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" exporter = AzureMonitorTraceExporter.from_connection_string(connection_string)\n",
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" provider.add_span_processor(BatchSpanProcessor(exporter))\n",
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" print(\"[otel] Azure Monitor trace exporter configured\")\n",
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" else:\n",
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" provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter()))\n",
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" if connection_string and AzureMonitorTraceExporter is None:\n",
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" print(\"[otel] Azure Monitor exporter unavailable. Install azure-monitor-opentelemetry-exporter\")\n",
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" else:\n",
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" print(\"[otel] Console span exporter configured\")\n",
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"\n",
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" trace.set_tracer_provider(provider)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"API_CONFIG = _resolve_api_config()\n",
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"_set_capture_env(API_CONFIG.provider, API_CONFIG.base_url)\n",
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"_configure_tracer()\n",
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"\n",
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"OpenAIAgentsInstrumentor().instrument(tracer_provider=trace.get_tracer_provider())\n",
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"CLIENT = API_CONFIG.build_client()\n",
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"set_tracing_disabled(False)\n",
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"print(\"Instrumentation ready for provider:\", API_CONFIG.provider)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"@function_tool\n",
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"def get_weather(city: str) -> dict[str, object]:\n",
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" LOGGER.info(\"Getting weather for %s\", city)\n",
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" if random.random() < 0.05:\n",
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" return {\"city\": city, \"temperature\": 72, \"description\": \"Sunny\"}\n",
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" return {\"city\": city, \"temperature\": 60, \"description\": \"Rainy\"}\n",
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"\n",
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"\n",
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"@function_tool\n",
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"def get_activities(city: str, date: str) -> list[dict[str, object]]:\n",
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" LOGGER.info(\"Getting activities for %s on %s\", city, date)\n",
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" return [\n",
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" {\"name\": \"Hiking\", \"location\": city},\n",
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" {\"name\": \"Beach\", \"location\": city},\n",
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" {\"name\": \"Museum\", \"location\": city},\n",
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" ]\n",
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"\n",
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"\n",
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"@function_tool\n",
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"def get_current_date() -> str:\n",
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" \"\"\"Gets the current date and returns as YYYY-MM-DD.\"\"\"\n",
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"\n",
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" LOGGER.info(\"Getting current date\")\n",
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" return datetime.now().strftime(\"%Y-%m-%d\")\n",
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"\n",
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"\n",
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"AGENT = Agent(\n",
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" name=\"Weekend Planner\",\n",
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" instructions=(\n",
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" \"You help users plan their weekends and choose the best activities for the given weather. \"\n",
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" \"If an activity would be unpleasant in the weather, do not recommend it. \"\n",
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" \"Always include the relevant weekend date in your response.\"\n",
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" ),\n",
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" tools=[get_weather, get_activities, get_current_date],\n",
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" model=OpenAIChatCompletionsModel(model=API_CONFIG.model_name, openai_client=CLIENT),\n",
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")\n",
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"print(\"Agent ready:\", AGENT.name)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Run the agent\n",
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"Execute the final cell to orchestrate a single planning session. Spans will be emitted to the configured exporter (console or Azure Monitor)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"async def run_planner():\n",
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" tracer = trace.get_tracer(__name__)\n",
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" with tracer.start_as_current_span(f\"weekend_planning_session[{API_CONFIG.provider}]\") as span:\n",
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" user_request = \"Hi, what can I do this weekend in Seattle?\"\n",
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" span.set_attribute(\"user.request\", user_request)\n",
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" span.set_attribute(\"gen_ai.provider.name\", API_CONFIG.provider)\n",
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" span.set_attribute(\"gen_ai.request.model\", API_CONFIG.model_name)\n",
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" span.set_attribute(\"agent.name\", AGENT.name)\n",
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" span.set_attribute(\"target.city\", \"Seattle\")\n",
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" try:\n",
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" result = await Runner.run(AGENT, input=user_request)\n",
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" output = result.final_output or \"\"\n",
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" span.set_attribute(\"agent.response\", output[:500])\n",
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" span.set_attribute(\"request.success\", True)\n",
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" print(output)\n",
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" except Exception as exc:\n",
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" span.record_exception(exc)\n",
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" span.set_attribute(\"request.success\", False)\n",
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" raise\n",
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"\n",
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"await run_planner()\n",
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"trace.get_tracer_provider().shutdown()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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