# SkillPatch skill: azure-ai-contentunderstanding-py

This skill provides guidance for using the Azure AI Content Understanding SDK for Python to extract semantic content from multimodal sources including documents, images, audio, and video. It covers authentication best practices using DefaultAzureCredential, environment setup, prebuilt analyzers, and the core async polling workflow for long-running analysis operations. Ideal for building RAG pipelines and automated content extraction workflows on Azure.

You (the agent) were given this URL and asked to use this skill. This is a **public** skill — no SkillPatch account, API key, or prior setup is required. Two ways to use it:

**1. Use it right now** — the skill's complete file tree (SKILL.md + all reference files) is inlined below; read `SKILL.md` first, then follow it, consulting the other files as it directs.

**2. Install the exact package onto disk** (recommended if you can run a shell — this reproduces the skill byte-for-byte, including any binary assets that can't be inlined):

```bash
mkdir -p .claude/skills/azure-ai-contentunderstanding-py
curl -sSL https://skillpatch.dev/install_skill/azure-ai-contentunderstanding-py | tar -xz -C .claude/skills/
```

(`.claude/skills/` is Claude Code's convention; use whatever directory your agent loads skills from.)


---

## Skill files (1)

- `SKILL.md`


### `SKILL.md`

````markdown
---
name: azure-ai-contentunderstanding-py
description: |
  Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.
  Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"
  package: azure-ai-contentunderstanding
---

# Azure AI Content Understanding SDK for Python

Multimodal AI service that extracts semantic content from documents, video, audio, and image files for RAG and automated workflows.

## Installation

```bash
pip install azure-ai-contentunderstanding
```

## Environment Variables

```bash
CONTENTUNDERSTANDING_ENDPOINT=https://<resource>.cognitiveservices.azure.com/  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```

## Authentication & Lifecycle

> **🔑 Two rules apply to every code sample below:**
>
> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
>    - Local dev: `DefaultAzureCredential` works as-is.
>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) to constrain the credential chain to production-safe credentials.
> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:
>    - Sync: `with <Client>(...) as client:`
>    - Async: `async with <Client>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)
>
> Snippets may abbreviate this setup, but production code should always follow both rules.

```python
import os
from azure.ai.contentunderstanding import ContentUnderstandingClient
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential

endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
with ContentUnderstandingClient(endpoint=endpoint, credential=credential) as client:
    analyzers = list(client.list_analyzers())
```

## Core Workflow

Content Understanding operations are asynchronous long-running operations:

1. **Begin Analysis** — Start the analysis operation with `begin_analyze()` (returns a poller)
2. **Poll for Results** — Poll until analysis completes (SDK handles this with `.result()`)
3. **Process Results** — Extract structured results from `AnalyzeResult.contents`

## Prebuilt Analyzers

| Analyzer | Content Type | Purpose |
|----------|--------------|---------|
| `prebuilt-documentSearch` | Documents | Extract markdown for RAG applications |
| `prebuilt-imageSearch` | Images | Extract content from images |
| `prebuilt-audioSearch` | Audio | Transcribe audio with timing |
| `prebuilt-videoSearch` | Video | Extract frames, transcripts, summaries |
| `prebuilt-invoice` | Documents | Extract invoice fields |

## Analyze Document

```python
import os
from azure.ai.contentunderstanding import ContentUnderstandingClient
from azure.ai.contentunderstanding.models import AnalyzeInput
from azure.identity import DefaultAzureCredential

endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
with ContentUnderstandingClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential()
) as client:
    # Analyze document from URL
    poller = client.begin_analyze(
        analyzer_id="prebuilt-documentSearch",
        inputs=[AnalyzeInput(url="https://example.com/document.pdf")]
    )

    result = poller.result()

    # Access markdown content (contents is a list)
    content = result.contents[0]
    print(content.markdown)
```

## Access Document Content Details

```python
from azure.ai.contentunderstanding.models import MediaContentKind, DocumentContent

content = result.contents[0]
if content.kind == MediaContentKind.DOCUMENT:
    document_content: DocumentContent = content  # type: ignore
    print(document_content.start_page_number)
```

## Analyze Image

```python
from azure.ai.contentunderstanding.models import AnalyzeInput

poller = client.begin_analyze(
    analyzer_id="prebuilt-imageSearch",
    inputs=[AnalyzeInput(url="https://example.com/image.jpg")]
)
result = poller.result()
content = result.contents[0]
print(content.markdown)
```

## Analyze Video

```python
from azure.ai.contentunderstanding.models import AnalyzeInput

poller = client.begin_analyze(
    analyzer_id="prebuilt-videoSearch",
    inputs=[AnalyzeInput(url="https://example.com/video.mp4")]
)

result = poller.result()

# Access video content (AudioVisualContent)
content = result.contents[0]

# Get transcript phrases with timing
for phrase in content.transcript_phrases:
    print(f"[{phrase.start_time} - {phrase.end_time}]: {phrase.text}")

# Get key frames (for video)
for frame in content.key_frames:
    print(f"Frame at {frame.time}: {frame.description}")
```

## Analyze Audio

```python
from azure.ai.contentunderstanding.models import AnalyzeInput

poller = client.begin_analyze(
    analyzer_id="prebuilt-audioSearch",
    inputs=[AnalyzeInput(url="https://example.com/audio.mp3")]
)

result = poller.result()

# Access audio transcript
content = result.contents[0]
for phrase in content.transcript_phrases:
    print(f"[{phrase.start_time}] {phrase.text}")
```

## Custom Analyzers

Create custom analyzers with field schemas for specialized extraction:

```python
# Create custom analyzer
analyzer = client.create_analyzer(
    analyzer_id="my-invoice-analyzer",
    analyzer={
        "description": "Custom invoice analyzer",
        "base_analyzer_id": "prebuilt-documentSearch",
        "field_schema": {
            "fields": {
                "vendor_name": {"type": "string"},
                "invoice_total": {"type": "number"},
                "line_items": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "description": {"type": "string"},
                            "amount": {"type": "number"}
                        }
                    }
                }
            }
        }
    }
)

# Use custom analyzer
from azure.ai.contentunderstanding.models import AnalyzeInput

poller = client.begin_analyze(
    analyzer_id="my-invoice-analyzer",
    inputs=[AnalyzeInput(url="https://example.com/invoice.pdf")]
)

result = poller.result()

# Access extracted fields
print(result.fields["vendor_name"])
print(result.fields["invoice_total"])
```

## Analyzer Management

```python
# List all analyzers
analyzers = client.list_analyzers()
for analyzer in analyzers:
    print(f"{analyzer.analyzer_id}: {analyzer.description}")

# Get specific analyzer
analyzer = client.get_analyzer("prebuilt-documentSearch")

# Delete custom analyzer
client.delete_analyzer("my-custom-analyzer")
```

## Async Client

```python
import asyncio
import os
from azure.ai.contentunderstanding.aio import ContentUnderstandingClient
from azure.ai.contentunderstanding.models import AnalyzeInput
from azure.identity.aio import DefaultAzureCredential

async def analyze_document():
    endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
    async with DefaultAzureCredential() as credential:
        async with ContentUnderstandingClient(
            endpoint=endpoint,
            credential=credential
        ) as client:
            poller = await client.begin_analyze(
                analyzer_id="prebuilt-documentSearch",
                inputs=[AnalyzeInput(url="https://example.com/doc.pdf")]
            )
            result = await poller.result()
            content = result.contents[0]
            return content.markdown

asyncio.run(analyze_document())
```

## Content Types

| Class | For | Provides |
|-------|-----|----------|
| `DocumentContent` | PDF, images, Office docs | Pages, tables, figures, paragraphs |
| `AudioVisualContent` | Audio, video files | Transcript phrases, timing, key frames |

Both derive from `MediaContent` which provides basic info and markdown representation.

## Model Imports

```python
from azure.ai.contentunderstanding.models import (
    AnalyzeInput,
    AnalyzeResult,
    MediaContentKind,
    DocumentContent,
    AudioVisualContent,
)
```

## Client Types

| Client | Purpose |
|--------|---------|
| `ContentUnderstandingClient` | Sync client for all operations |
| `ContentUnderstandingClient` (aio) | Async client for all operations |

## Best Practices

1. **Pick sync OR async and stay consistent.** Do not mix `azure.ai.contentunderstanding` sync clients with `azure.ai.contentunderstanding.aio` async clients in the same call path. Choose one mode per module.
2. **Always use context managers for clients and async credentials.** Wrap every client in `with ContentUnderstandingClient(...) as client:` (sync) or `async with ContentUnderstandingClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
3. **Use `begin_analyze` with `AnalyzeInput`** — this is the correct method signature
4. **Access results via `result.contents[0]`** — results are returned as a list
5. **Use prebuilt analyzers** for common scenarios (document/image/audio/video search)
6. **Create custom analyzers** only for domain-specific field extraction
7. **Use async client** for high-throughput scenarios with `azure.identity.aio` credentials
8. **Handle long-running operations** — video/audio analysis can take minutes
9. **Use URL sources** when possible to avoid upload overhead

````