feat(closes OPEN-10097): create Azure Content Understanding tracer

examples/tracing/azure-content-understanding/azure_content_understanding_tracing.ipynb

@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2722b419",
+   "metadata": {},
+   "source": [
+    "[![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",
+    "\n",
+    "\n",
+    "# <a id=\"top\">Azure Content Understanding tracing quickstart</a>\n",
+    "\n",
+    "This notebook illustrates how to get started monitoring Azure Content Understanding with Openlayer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "020c8f6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install openlayer azure-ai-contentunderstanding azure-identity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75c2a473",
+   "metadata": {},
+   "source": [
+    "## 1. Set the environment variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3f4fa13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Azure Content Understanding env variables\n",
+    "os.environ[\"AZURE_CONTENT_UNDERSTANDING_ENDPOINT\"] = \"YOUR_AZURE_CONTENT_UNDERSTANDING_ENDPOINT_HERE\"\n",
+    "os.environ[\"AZURE_CONTENT_UNDERSTANDING_KEY\"] = \"YOUR_AZURE_CONTENT_UNDERSTANDING_KEY_HERE\"\n",
+    "\n",
+    "# Openlayer env variables\n",
+    "os.environ[\"OPENLAYER_API_KEY\"] = \"YOUR_OPENLAYER_API_KEY_HERE\"\n",
+    "os.environ[\"OPENLAYER_INFERENCE_PIPELINE_ID\"] = \"YOUR_OPENLAYER_INFERENCE_PIPELINE_ID_HERE\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9758533f",
+   "metadata": {},
+   "source": [
+    "## 2. Import the `trace_azure_content_understanding` function and create the client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e60584fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azure.core.credentials import AzureKeyCredential\n",
+    "from azure.ai.contentunderstanding import ContentUnderstandingClient\n",
+    "from azure.ai.contentunderstanding.models import AnalysisInput\n",
+    "\n",
+    "from openlayer.lib import configure, trace_azure_content_understanding\n",
+    "\n",
+    "# Configure if you want to upload documents to Openlayer storage\n",
+    "configure(\n",
+    "    attachment_upload_enabled=True,   # upload binary/file attachments\n",
+    "    url_upload_enabled=True,          # also download & re-upload external URLs\n",
+    ")\n",
+    "\n",
+    "client = trace_azure_content_understanding(\n",
+    "    ContentUnderstandingClient(\n",
+    "        endpoint=os.environ.get(\"AZURE_CONTENT_UNDERSTANDING_ENDPOINT\"),\n",
+    "        credential=AzureKeyCredential(os.environ.get(\"AZURE_CONTENT_UNDERSTANDING_KEY\")),\n",
+    "        api_version=\"2025-11-01\",\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72a6b954",
+   "metadata": {},
+   "source": [
+    "## 3. Use your traced client normally"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76a350b4",
+   "metadata": {},
+   "source": [
+    "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!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e00c1c79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analyzer_id = \"prebuilt-read\"\n",
+    "url = \"https://contentunderstanding.ai.azure.com/assets/prebuilt/read_healthcare.png\"\n",
+    "\n",
+    "poller = client.begin_analyze(\n",
+    "    analyzer_id=analyzer_id,\n",
+    "    inputs=[AnalysisInput(url=url)],\n",
+    ")\n",
+    "result = poller.result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abaf6987-c257-4f0d-96e7-3739b24c7206",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hr-benefits",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/openlayer/lib/__init__.py

@@ -11,6 +11,7 @@
     "trace_async_openai",
     "trace_async",
     "trace_bedrock",
+    "trace_azure_content_understanding",
     "trace_oci_genai",
     "trace_oci",  # Alias for backward compatibility
     "trace_litellm",
@@ -138,6 +139,26 @@ def trace_bedrock(client):
     return bedrock_tracer.trace_bedrock(client)
 
 
+def trace_azure_content_understanding(client):
+    """Trace Azure Content Understanding analyses."""
+    # pylint: disable=import-outside-toplevel
+    try:
+        from azure.ai.contentunderstanding import ContentUnderstandingClient
+    except ImportError:
+        raise ImportError(
+            "azure-ai-contentunderstanding is required for Azure Content Understanding tracing. "
+            "Install with: pip install azure-ai-contentunderstanding"
+        )
+
+    from .integrations import azure_content_understanding_tracer
+
+    if not isinstance(client, ContentUnderstandingClient):
+        raise ValueError(
+            "Invalid client. Please provide a ContentUnderstandingClient."
+        )
+    return azure_content_understanding_tracer.trace_azure_content_understanding(client)
+
+
 def trace_oci_genai(client, estimate_tokens: bool = True):
     """Trace OCI GenAI chat completions.