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feat(closes OPEN-10097): create Azure Content Understanding tracer
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‎examples/tracing/azure-content-understanding/azure_content_understanding_tracing.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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"id": "2722b419",
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"metadata": {},
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"source": [
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openlayer-ai/openlayer-python/blob/main/examples/tracing/azure-content-understanding/azure_content_understanding_tracing.ipynb)\n",
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"\n",
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"\n",
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"# <a id=\"top\">Azure Content Understanding tracing quickstart</a>\n",
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"\n",
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"This notebook illustrates how to get started monitoring Azure Content Understanding with Openlayer."
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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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"id": "020c8f6a",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install openlayer azure-ai-contentunderstanding azure-identity"
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]
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},
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{
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"cell_type": "markdown",
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"id": "75c2a473",
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"metadata": {},
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"source": [
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"## 1. Set the environment variables"
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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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"id": "f3f4fa13",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# Azure Content Understanding env variables\n",
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"os.environ[\"AZURE_CONTENT_UNDERSTANDING_ENDPOINT\"] = \"YOUR_AZURE_CONTENT_UNDERSTANDING_ENDPOINT_HERE\"\n",
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"os.environ[\"AZURE_CONTENT_UNDERSTANDING_KEY\"] = \"YOUR_AZURE_CONTENT_UNDERSTANDING_KEY_HERE\"\n",
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"\n",
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"# Openlayer env variables\n",
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"os.environ[\"OPENLAYER_API_KEY\"] = \"YOUR_OPENLAYER_API_KEY_HERE\"\n",
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"os.environ[\"OPENLAYER_INFERENCE_PIPELINE_ID\"] = \"YOUR_OPENLAYER_INFERENCE_PIPELINE_ID_HERE\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "9758533f",
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"metadata": {},
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"source": [
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"## 2. Import the `trace_azure_content_understanding` function and create the client"
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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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"id": "e60584fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"from azure.core.credentials import AzureKeyCredential\n",
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"from azure.ai.contentunderstanding import ContentUnderstandingClient\n",
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"from azure.ai.contentunderstanding.models import AnalysisInput\n",
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"\n",
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"from openlayer.lib import configure, trace_azure_content_understanding\n",
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"\n",
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"# Configure if you want to upload documents to Openlayer storage\n",
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"configure(\n",
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" attachment_upload_enabled=True, # upload binary/file attachments\n",
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" url_upload_enabled=True, # also download & re-upload external URLs\n",
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")\n",
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"\n",
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"client = trace_azure_content_understanding(\n",
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" ContentUnderstandingClient(\n",
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" endpoint=os.environ.get(\"AZURE_CONTENT_UNDERSTANDING_ENDPOINT\"),\n",
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" credential=AzureKeyCredential(os.environ.get(\"AZURE_CONTENT_UNDERSTANDING_KEY\")),\n",
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" api_version=\"2025-11-01\",\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72a6b954",
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"metadata": {},
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"source": [
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"## 3. Use your traced client normally"
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]
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},
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{
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"cell_type": "markdown",
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"id": "76a350b4",
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"metadata": {},
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"source": [
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"That's it! Now you can continue using your Azure Content Understanding client normally. The data is automatically published to Openlayer and you can start creating tests around it!"
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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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"id": "e00c1c79",
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"metadata": {},
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"outputs": [],
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"source": [
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"analyzer_id = \"prebuilt-read\"\n",
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"url = \"https://contentunderstanding.ai.azure.com/assets/prebuilt/read_healthcare.png\"\n",
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"\n",
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"poller = client.begin_analyze(\n",
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" analyzer_id=analyzer_id,\n",
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" inputs=[AnalysisInput(url=url)],\n",
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")\n",
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"result = poller.result()"
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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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"id": "abaf6987-c257-4f0d-96e7-3739b24c7206",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "hr-benefits",
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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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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.13"
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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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}

‎src/openlayer/lib/__init__.py‎

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"trace_async_openai",
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"trace_async",
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"trace_bedrock",
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"trace_azure_content_understanding",
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"trace_oci_genai",
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"trace_oci", # Alias for backward compatibility
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"trace_litellm",
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return bedrock_tracer.trace_bedrock(client)
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def trace_azure_content_understanding(client):
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"""Trace Azure Content Understanding analyses."""
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# pylint: disable=import-outside-toplevel
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try:
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from azure.ai.contentunderstanding import ContentUnderstandingClient
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except ImportError:
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raise ImportError(
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"azure-ai-contentunderstanding is required for Azure Content Understanding tracing. "
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"Install with: pip install azure-ai-contentunderstanding"
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)
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from .integrations import azure_content_understanding_tracer
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if not isinstance(client, ContentUnderstandingClient):
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raise ValueError(
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"Invalid client. Please provide a ContentUnderstandingClient."
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)
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return azure_content_understanding_tracer.trace_azure_content_understanding(client)
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def trace_oci_genai(client, estimate_tokens: bool = True):
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"""Trace OCI GenAI chat completions.
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