> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Azure Content Understanding
> Learn how to monitor Azure Content Understanding with Openlayer
Openlayer integrates with [Azure Content Understanding](https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/overview), Microsoft's service for extracting structured data and insights from documents, images, audio, and video using LLMs.
## Monitoring Azure Content Understanding
To use [monitoring mode](/monitoring/overview), instrument your code to publish the analysis requests your AI system makes to the Openlayer platform. Each `begin_analyze` → `poller.result()` call is automatically traced and published with inputs, outputs, latency, token usage, and the underlying model used.
### Setup
Instrument your client:
```python Python theme={null}
import os
from azure.ai.contentunderstanding import ContentUnderstandingClient
from azure.ai.contentunderstanding.models import AnalysisInput
from azure.core.credentials import AzureKeyCredential
# 1. Set the environment variables
os.environ["OPENLAYER_API_KEY"] = "YOUR_OPENLAYER_API_KEY_HERE"
os.environ["OPENLAYER_INFERENCE_PIPELINE_ID"] = "YOUR_OPENLAYER_INFERENCE_PIPELINE_ID_HERE"
# 2. Wrap the Content Understanding client with `trace_azure_content_understanding`
from openlayer.lib import trace_azure_content_understanding, configure
# Configure if you want to upload documents to Openlayer storage
configure(
attachment_upload_enabled=True, # upload binary/file attachments
url_upload_enabled=True, # also download & re-upload external URLs
)
client = trace_azure_content_understanding(
ContentUnderstandingClient(
endpoint="YOUR_AZURE_CONTENT_UNDERSTANDING_ENDPOINT_HERE",
credential=AzureKeyCredential("YOUR_AZURE_CONTENT_UNDERSTANDING_KEY_HERE"),
api_version="2025-11-01",
)
)
# 3. Use the client normally — tracing happens automatically
poller = client.begin_analyze(
analyzer_id="prebuilt-invoice",
inputs=[AnalysisInput(url="https://example.com/invoice.pdf")],
)
result = poller.result()
```
Once instrumented, every analysis call is automatically published to Openlayer.
In the "Data" page of your Openlayer data source, you can see the traces for each request.
After your AI system requests are continuously published and logged by Openlayer, you can
[create tests](/tests/overview) that run at a regular cadence on top of them.
To store the source files alongside your traces in Openlayer, enable
attachment uploads in `configure()` by setting
`attachment_upload_enabled=True` (for binary inputs) and
`url_upload_enabled=True` (to also fetch and persist URL-referenced files).